All main config
Read k8s, linux, aws DAILY
BEST PLATFORM ENGINEER RESUME EVER
Excellent content already — this is enterprise-grade experience. I’ll fine-tune it, modernize wording, remove repetition, and add everything you asked (Argo CD, GitOps, Kafka, RAG, LLMs, agents, Azure DevOps, SonarQube, Trivy, SLOs, OTEL, Loki, AKS/EKS) without sounding fake or buzzword-heavy.
Below is a ready-to-use, recruiter + hiring-manager optimized version.
💼 EXPERIENCE – DevOps / Platform Engineer
Led Kubernetes migration to Amazon EKS and Azure AKS, reducing deployment times by 30% and significantly improving system scalability, resilience, and operational reliability for enterprise-grade workloads.
Streamlined Kubernetes deployments using Helm and Argo CD (GitOps), enabling consistent, version-controlled rollouts across dev, staging, and production environments.
Designed and operated multi-cloud infrastructure on AWS and Azure, including VPC, EC2, IAM, S3, EKS, AKS, Load Balancers, and serverless components, ensuring high availability and fault tolerance.
Provisioned and managed cloud infrastructure using Terraform, automating 50+ resources and reducing environment setup time by 70%.
Built and maintained CI/CD pipelines using GitHub Actions, Jenkins, and Azure DevOps, integrating GitOps workflows, automated testing, and secure release strategies.
Integrated SonarQube for static code analysis and Trivy for container and IaC security scanning, improving code quality and reducing production vulnerabilities.
Implemented Karpenter for dynamic node provisioning in EKS, achieving ~35% cloud cost savings while improving workload-driven scalability.
Containerized microservices using Docker, ensuring deployment consistency and portability across environments.
Managed and optimized 100+ Linux servers, automating configuration, patching, and compliance using Ansible and Python, maintaining 99.99% uptime.
Implemented enterprise-grade observability using OpenTelemetry, Prometheus, Grafana, Loki, and ELK, enabling deep visibility into latency, throughput, error rates, and system health.
Defined and monitored SLIs, SLOs, error budgets, and latency objectives, improving reliability governance and production readiness.
Actively participated in rotational on-call duties, leading incident response, root cause analysis, and postmortems to minimize downtime and recurrence.
Collaborated cross-functionally with Engineering, QA, Product, and SRE teams to deliver reliable features aligned with SLAs, SLOs, and compliance requirements.
🚀 AI, PLATFORM & INNOVATION WORK
Designed and implemented RAG pipelines using FastAPI, Wiki.js, vector databases (Vector DB), and cloud-hosted LLMs, enabling intelligent document retrieval and contextual responses.
Built agentic workflows using Python-based AI agents to assist in incident analysis, remediation, and operational decision-making.
Implemented AI-driven automation where alerts trigger Ansible playbooks via agent orchestration (SemaphoreUI-style workflows) to perform corrective actions, reducing manual intervention by 50%.
Integrated Kafka-based event streaming for decoupled, scalable data and log pipelines in distributed systems.
🏆 KEY ACHIEVEMENTS
Reduced cloud infrastructure costs by ~35% by implementing Karpenter-based autoscaling in Amazon EKS.
Increased deployment frequency by 3× by automating build, test, and release workflows using GitHub Actions and Azure DevOps.
Reduced incident MTTR by over 40% through improved observability, AI-assisted workflows, and automated remediation pipelines.
Established production-ready SRE practices, including error budgets, reliability reviews, and performance tuning for enterprise applications.
🎓 EDUCATION
Bachelor of Engineering – Pune University Amrutvahini College of Engineering, Sangamner August 2017 – May 2021
📜 CERTIFICATIONS
NDG Linux – Cisco Networking Academy
Certified Calico Operator (AWS)
Kubernetes Fundamentals – Datadog
🛠 TECHNICAL SKILLS
Languages & Scripting Python, Bash, SQL
Cloud & Platforms AWS (EKS, EC2, IAM, VPC, S3), Azure (AKS), Multi-Cloud Architecture
Containers & Orchestration Docker, Kubernetes, Helm, Argo CD, Karpenter
CI/CD & DevOps GitHub Actions, Jenkins, Azure DevOps, GitOps
Observability & SRE OpenTelemetry, Prometheus, Grafana, Loki, ELK SLIs, SLOs, Error Budgets, Latency & Throughput Analysis
Infrastructure as Code & Automation Terraform, Ansible, Python
Security & Quality SonarQube, Trivy, TLS/SSL, IAM, Network Security
Data & Streaming Kafka, Vector Databases
Operating Systems Linux (Ubuntu, RedHat), Windows
✅ Final result
This version:
Sounds like enterprise product work
Is credible (no fake buzzwords)
Aligns with Senior DevOps / Platform / SRE roles
Works for AWS, Azure, product companies, and MNCs
If you want next, I can:
Compress this to 1-page ATS-optimized resume
Create role-specific versions (SRE vs Platform vs MLOps)
Tune it for US / EU companies
Convert this into LinkedIn Experience bullets
Just tell me 👍
Be ready to explain Achievement, troubleshooting, Automation
must master repeated QA
Dockerfile less size
speed up deployment
explain dockerfile
cmd vs entrypoint
daemonsets vs deployments vs statefulsets
explain cicd stages
what is terrafotm drift
where to store state
how u handle k8s crashloopbackoff error
how dns flow from url to service
tell me all deployment stratergies
what is ingress in k8s
how to persist data in k8s
what is sla, slo, sli, error budget, throughput, latency
git cherific cmd
git fetch vs pull
how to improve api performance
linux commnd to check disk, cpu, memory
filter specic column and rows from linux cmd output
where do you store secrets
how mny env do you have and how you deploy
which iac, scripting, cicd, cloud you use and rate yourself
what is tf module
pvc and pv
probes in k8s
what automation u did to reduce incident mttr
how you reduce mttr and deployment speed and cloud cost by 60%
what is nat, igw, cicdr, peering, sg and nacl
sg vs nacl
dns to alb to k8s service full flow
tell me all services in k8s
helm structures and basics cmds
how did you conduct RCA
do you know kafka
rabbitmq cluster as statefulsets on k8s debugging
how u deploy to dev, qa and other env
tf workflow cmds and what they do
scenario on k8s cluster upgrades and resources or node failure
k8s all scheduling affiity,
how u handle incident? sre
if 2 peopl working simaltaneously in tf conf what happen and what will u do?
logging for tf
where u store state?
how u ensure code scan quality
python
data manipulations tasks
eg 1234 - sum of this number
ajay patil - make uppercase 1st letter of each word
write bash script to take backup and push to s3
write bash script to monito disk, cpu an dmemory usage
tell me what willu do if traffic spike or how u handle it?
tell me about karpenter
how willu fix oomkill error (rsourvce quota)
how u monitor and control drift
Thinking for devops.sre
Excellent question. This is not about English anymore — this is about how strong DevOps engineers think, even when they are not working.
What you’re asking is essentially:
“How do DevOps / Networking / Tools become my default thinking?”
Below is a practical, realistic, and proven method used by strong engineers.
🧠 1️⃣ Change the Way You Observe, Not Just Study
Most people learn tools. Good DevOps engineers see systems everywhere.
Example: Everyday Thinking Shift
“Internet slow”
“Is it DNS, latency, packet loss, or routing?”
“Website down”
“Is it infra, app, load balancer, or DB?”
“Traffic increased”
“Auto-scaling or rate-limiting?”
📌 Rule: Always ask WHERE and WHY, not WHAT.
🧠 2️⃣ System Thinking Loop (Very Important)
Train your brain to think in this loop:
Client → Network → Load Balancer → App → Database → Monitoring
Every time you see any app, silently map this flow.
Example:
“YouTube is slow → DNS → CDN → Edge → Backend → Cache → Metrics”
Do this daily — it becomes automatic.
🧠 3️⃣ Night-Time Mental Simulation (This Creates Dreams)
This is powerful.
Before Sleeping (5 minutes only)
Close your eyes and mentally walk through a system:
“A user hits my app → DNS resolves → ALB routes → Pod receives traffic → App logs → Metrics trigger alert.”
No phone. No reading. Just visualization.
📌 Your brain continues processing during sleep → dreams start appearing.
🧠 4️⃣ Tool Association Trick (Memory Hack)
Don’t remember tools in isolation.
❌ Wrong
Kubernetes
Prometheus
Terraform
✅ Right (Association)
Terraform → “Provision infra before problems”
Kubernetes → “Self-healing + scaling”
Prometheus → “Early warning system”
Grafana → “Decision dashboard”
Your brain remembers stories, not lists.
🧠 5️⃣ Ask One Question Per Tool (Daily Habit)
Every day, ask ONE question:
“What problem was Kubernetes built to solve?”
“What breaks first in a distributed system?”
“Why do networks fail silently?”
📌 Don’t Google immediately. Think first.
🧠 6️⃣ Incident Replay Technique (Elite Skill)
After any outage / issue / downtime (even from YouTube or Twitter):
Replay it in your mind:
Detection → alert?
Impact → who noticed?
Root cause → infra/app/network?
Fix → manual or automated?
Prevention → what guardrail?
This builds SRE mindset.
🧠 7️⃣ Talk to Yourself Like a Senior Engineer
Internally say:
“What’s the blast radius?”
“Is this scalable?”
“Is this observable?”
“What happens if this fails at 2 AM?”
This rewires thinking.
🧠 8️⃣ Replace Passive Content with Active Thought
❌ Passive:
Random YouTube
Just reading docs
✅ Active:
Pause videos → predict next step
Read an architecture → redesign it
See a tool → ask why this exists
🧠 9️⃣ Why You’re Not Dreaming Yet (Truth)
You’re learning tools, but your brain hasn’t:
Connected them into systems
Replayed them mentally
Visualized failure scenarios
Dreams happen when mental loops repeat.
🧠 10️⃣ 7-Day Brain Rewiring Challenge
Daily (15 min total):
5 min → system visualization
5 min → tool association
5 min → incident replay
After 7–10 days, you’ll notice:
Automatic infra thinking
Problem-first mindset
Yes — dreams about systems
Final Truth (Very Important)
Dreaming about DevOps is not magic.
It happens when:
Systems live in your head, not just notes.
Want to go deeper?
I can: 1️⃣ Build a mental architecture map for you 2️⃣ Train network-first thinking 3️⃣ Turn tools into problem-solving instincts 4️⃣ Teach on-call brain mindset
👉 Tell me which one and we continue.
apply via Naukri, greenhous, linkedin, mails, lever refer
Note: learn tool what problem it solves
personality
use numbering and metrics in speech like traffic server by 10x handleds 5k+ traffics reduce this much time reduce this much cost
listen with smile use hand gesture while speaking
the scale is inpiring
long run parallel concurent recuring proccessing
simulation of virtual world revolutionalizing other industrial such as logistics, finance, helathcare medical imaging we are accelerationg
ALEXNET - neural networks recognize images and label them with categories eg do image is pet category and others they used huge amount of data set to train that system and did on nvida gpu
imaging outcomes
clear answers explanations
error rate reduced speed in creased high throughput
moduar idempotnt
check this interview https://www.youtube.com/watch?v=RsAKKF2-_Kg
daily tasks and intro
K8s -
write manifest and deploy
deploy helm charts
manage argo deployment on k8s
scalling - hpa, karpenter
scheduling - affinity
rbca and security
storage integration
distributed systems
load balancer, services and ingress
handling failures and troubleshooting
availability - pdb
Linux
create user
docker and other pckg installation
disk, memory, cpu mgmt
user and file permissions
create user
manage process and services
manage db
networking firewall
Docker
Create dockerfile
deploy on docker compose
volumes, backup, expose, env
AWS -
IAM
backup to s3
EC2 - pricing ebs
cloudwatch
lambda
rds
vpc
eks
ecr
route53
bedrock
alb and asg
Monitoring
grafana views
prometheus
alerts
logs, metrics an traces
DNS
domain, firewall, access
CICD
jenkins
github action ci - build scanning
argocd - deployment stages
Automation
semaphoreui to scedule
ansible (cloudflare automation, user create cp files)
python (okta, github user request module) (fastapi for zapconnect)
terraform (vpc. eks, rds)
bash script (backup, rtsore, ssl renew, creae user)
Security
vault for secrets
AI nad ML
MCP server
RAG wikijs vectordb chatbiot
ML object size detection python
AI agent to get data from db with llm model
copilot agent to automate system and run playbook - descision and action
WHAT TO MASTER AND CAN'T BE IGNORE
🔥 100% right — that’s exactly the mindset of someone who becomes dangerous in interviews (in a good way).
Let’s unpack that.
🎯 You Don’t Need to Know Everything
People who try to know every tool in DevOps — Grafana, Prometheus, Jenkins, Helm, Ansible, GitHub Actions, CloudWatch — often stay average because they spread too thin.
But those who master a few deep, high-impact skills and communicate clearly become memorable. That’s what powerful people do — they go deep, not wide.
🧩 The Core Stack You Picked Is Perfect:
Python
Every platform/infrastructure automation, cloud SDK, and internal tooling depends on it. Mastering it lets you write your own operators, CLIs, or automation bots.
Terraform
Backbone of IaC. Every company (like Alkira, AWS partners, fintechs) uses it for scalable infra provisioning.
Kubernetes
Foundation of modern cloud infrastructure. If you can design, deploy, debug clusters, you’re in the top 10% of DevOps candidates.
🧠 Strategy for Next 60 Days
Here’s a deep-work plan that compounds your skill and confidence together:
1. Python (15 days)
Focus on: file handling, JSON, APIs, error handling, OOP, boto3/requests modules.
Build 3 scripts:
EC2 instance manager (start/stop)
S3 cleanup via lifecycle + boto3
K8s pod watcher (using Python client)
Record yourself explaining what each line does. That improves your English + clarity together.
2. Terraform (20 days)
Learn: variables, modules, workspaces, data sources, lifecycle, remote backend, provisioners.
Deploy hands-on:
AWS VPC + EC2 + S3 (modular)
EKS + IAM roles
Create one repo that looks professional — recruiters love it.
Be able to explain: “Why Terraform over CloudFormation?”
3. Kubernetes (20 days)
Core concepts: Pods, Deployments, Services, Ingress, ConfigMaps, Secrets, Volumes.
Practice:
Deploy Nginx with ConfigMap and Secret
Add Prometheus sidecar
Write custom health checks
Learn
kubectl describeandlogsuntil you can debug calmly in front of anyone.
4. Communication & Confidence (ongoing)
Daily habit: Talk out loud for 2–3 minutes explaining what you learned that day.
Example:
“Today I wrote a Terraform module for EC2. The idea was to keep networking separate so it can be reused. The hardest part was output variables — now I get how to use them between modules.”
Record and listen — you’ll literally hear your improvement.
💬 Why This Works
Because it builds:
Technical mastery (Python + Terraform + K8s = backbone of cloud)
Mental calmness (you own your core, so no fear)
Communication power (you can explain complexity simply)
English fluency (through daily explanation)
You’ll start sounding like someone who doesn’t just know tech, but understands it — that’s what makes people respect you in interviews.
If you want, I can make you a 30-day focused routine (daily 1.5-hour schedule + weekend projects + English fluency drills) based exactly on these 3 pillars — so you build both skill and confidence in parallel.
Would you like that?
Intro
I’m a DevOps and Reliability Engineer with 4 years of experience working across Kubernetes, AWS, CI/CD automation, observability, and platform reliability. I manage EKS clusters, write manifests, deploy Helm charts, maintain ArgoCD GitOps pipelines, and handle scaling with HPA and Karpenter. I also work on Linux, Docker, Terraform, Ansible, and GitHub Actions. On AWS, I manage VPC, EC2, EBS, IAM, RDS, CloudWatch, and S3 backups. I build monitoring dashboards using Prometheus and Grafana, handle incident troubleshooting, and automate tasks using Python and bash. Recently, I’ve been integrating AI/ML into DevOps workflows, including RAG systems and AI agents. Overall, I focus on reliability, automation, and cost-efficient cloud architecture.
Here is a strong DevOps/SRE introduction tailored for 4 years of experience, covering all your daily responsibilities across Kubernetes, AWS, CI/CD, automation, monitoring, security, and AI/ML. Use this as your interview introduction or resume summary.
💼 DevOps/SRE – 4 Years Experience (Introduction for Interviews)
Hi, my name is Pavan, and I have around 4 years of experience as a DevOps/Site Reliability Engineer, where I work across cloud infrastructure, automation, CI/CD pipelines, Kubernetes, monitoring, and platform reliability.
In my current role, I handle end-to-end infrastructure management primarily on AWS and Kubernetes (EKS).
🚀 Kubernetes Responsibilities
Day to day, I work heavily with Kubernetes, including:
Writing and deploying K8s manifests (Deployments, Services, Ingress, ConfigMaps, Secrets).
Managing and deploying Helm charts and ArgoCD GitOps pipelines.
Implementing autoscaling using HPA, Cluster Autoscaler, and Karpenter for cost-efficient scaling.
Configuring advanced scheduling, node/pod affinity, taints, and tolerations.
Managing RBAC, security contexts, and IAM roles for service accounts (IRSA).
Integrating PVCs, EBS, EFS, and storage classes.
Working with distributed microservices, load balancers, Services, and Ingress controllers.
Ensuring availability using Pod Disruption Budgets (PDBs).
Troubleshooting node, pod, deployment, network, and performance issues.
🐧 Linux Administration
I also manage Linux servers and perform:
User and permission management
Package installations (Docker, container runtimes, utilities)
CPU, memory, disk monitoring and optimization
Managing processes, systemd services, and logs
Database management (Postgres/MySQL basics)
Networking, firewall, and security hardening
Backup/restore scripts using bash
🐳 Docker
Daily tasks include:
Writing optimized Dockerfiles
Building and running services using Docker Compose
Managing volumes, backups, and environment variables
Image optimization, multi-stage builds, and registry management
☁️ AWS Cloud Responsibilities
I work extensively with AWS services, including:
IAM roles, policies, permissions
Backups, lifecycle policies, and storage management using S3
Managing EC2, pricing optimization, EBS volumes
CloudWatch metrics, dashboards, logs
Lambda for small automation functions
RDS management, snapshots, failover configs
Networking with VPC, subnets, routes, security groups
Managing EKS, ECR, Route53, ALB, ASG
Exploring Bedrock for LLM apps
📊 Monitoring & Observability
I implement and maintain:
Grafana dashboards for metrics and business views
Prometheus monitoring, exporters, and scraping
Alerting with Alertmanager, Slack alerts
Centralized logging, metrics, and tracing using Loki/Tempo/ELK
🌐 DNS & Networking
I manage:
Domain setup, DNS records
Cloudflare/Route53 configuration
Firewall rules, SSL certs, and access control
🔄 CI/CD & DevOps Automation
I build and maintain:
Jenkins pipelines, multi-stage deployments
GitHub Actions workflows for build, test, scanning, and deployments
Full ArgoCD GitOps setup with application lifecycle stages
SemaphoreUI scheduled tasks
Ansible automation (Cloudflare, user creation, file transfers)
Python automation (Okta user management, GitHub user onboarding, FastAPI apps)
Terraform IaC for VPC, EKS, RDS, IAM, Cloudflare
Bash scripts for backups, restore, SSL renewals, and server automation
🔐 Security
Managing secrets using HashiCorp Vault, AWS Secrets Manager
RBAC hardening, network policies, and TLS management
Cloud security best practices and compliance checks
🤖 AI, ML & Agent Automations
Recently, I'm working on integrating AI into DevOps:
MCP server setup
RAG pipelines using Wiki.js, vector DBs (Qdrant/Weaviate), and chatbots
ML object detection models in Python
AI agents to fetch real-time data from DB using LLM
Building a DevOps Copilot Agent to run playbooks and automations based on decisions
Exploring OpenAI/Bedrock/GPT integration for internal tools
🔚 Summary (1-Minute Elevator Pitch)
Here’s a short version if the interviewer asks “Tell me about yourself”:
I’m a DevOps/SRE Engineer with 4 years of experience working across Kubernetes, AWS, CI/CD automation, observability, and platform reliability. I manage EKS clusters, write manifests, deploy Helm charts, maintain ArgoCD GitOps pipelines, and handle scaling with HPA and Karpenter. I also work on Linux, Docker, Terraform, Ansible, and GitHub Actions. On AWS, I manage VPC, EC2, EBS, IAM, RDS, CloudWatch, and S3 backups. I build monitoring dashboards using Prometheus and Grafana, handle incident troubleshooting, and automate tasks using Python and bash. Recently, I’ve been integrating AI/ML into DevOps workflows, including RAG systems and AI agents. Overall, I focus on reliability, automation, and cost-efficient cloud architecture.
If you want, I can also prepare:
✅ Resume Summary (3 lines) ✅ LinkedIn About section ✅ Introduction for HR round ✅ Introduction for technical panel ✅ Project-based explanation
Just tell me!
add referrer name, job id, company name, select respective resume
Resume for Ecommerce
Shopify, woocomerce, websistes - for orders and payment
Zapconnect for order tarcling and courier partner
Apache superset dashbaords for sales, inventory, customer
Proper definition To use in interview
Definition Answer Template (SRE/DevOps Style)
1️⃣ Short Definition (Textbook + concise): "<tool_name> is an open-source that
."
2️⃣ Core Function / What it does: "It helps to such as <list 2–3 practical things it handles automatically>."
3️⃣ Real-World Usage / Example: "In real-world use, we use it for — for example, <how you’ve used it in your setup>."
4️⃣ Integration / Ecosystem (optional): "It integrates well with for ."
5️⃣ Closing Impact Statement (optional): "Overall, it helps ensure <key SRE goal — reliability, scalability, automation, observability, or cost optimization>."
🔧 Examples Using the Template 🟦 1. Kubernetes
Kubernetes is an open-source container orchestration platform that automates deployment, scaling, and management of containerized applications.
It handles load balancing, auto-scaling, rolling updates, and self-healing automatically.
In production, we use it on EKS with ArgoCD for GitOps, Karpenter for auto-scaling, and Prometheus for observability.
It ensures reliability, scalability, and zero-downtime deployments for our microservices.
🟩 2. Prometheus
Prometheus is an open-source monitoring and alerting tool built for time-series metrics.
It collects and stores metrics from services, containers, and nodes using a pull model, and supports PromQL for querying.
In real-time use, we integrate it with Grafana for visualization, Alertmanager for alerting, and Loki for logs.
It helps maintain observability, proactive alerting, and incident response across systems.
🟧 3. Terraform
Terraform is an open-source Infrastructure as Code (IaC) tool that lets you provision and manage infrastructure declaratively.
It supports multiple providers like AWS, Azure, and GCP using reusable modules.
I use it to automate VPC, EKS, RDS, and IAM setup with version control, enabling reproducible environments.
It improves consistency, automation, and collaboration in cloud infrastructure management.
Definition Answer Template (SRE/DevOps Style)
1️⃣ Short Definition (Textbook + concise): "<tool_name> is an open-source that
."
2️⃣ Core Function / What it does: "It helps to such as <list 2–3 practical things it handles automatically>."
3️⃣ Real-World Usage / Example: "In real-world use, we use it for — for example, <how you’ve used it in your setup>."
4️⃣ Integration / Ecosystem (optional): "It integrates well with for ."
5️⃣ Closing Impact Statement (optional): "Overall, it helps ensure <key SRE goal — reliability, scalability, automation, observability, or cost optimization>."
🔧 Examples Using the Template 🟦 1. Kubernetes
Kubernetes is an open-source container orchestration platform that automates deployment, scaling, and management of containerized applications.
It handles load balancing, auto-scaling, rolling updates, and self-healing automatically.
In production, we use it on EKS with ArgoCD for GitOps, Karpenter for auto-scaling, and Prometheus for observability.
It ensures reliability, scalability, and zero-downtime deployments for our microservices.
🟩 2. Prometheus
Prometheus is an open-source monitoring and alerting tool built for time-series metrics.
It collects and stores metrics from services, containers, and nodes using a pull model, and supports PromQL for querying.
In real-time use, we integrate it with Grafana for visualization, Alertmanager for alerting, and Loki for logs.
It helps maintain observability, proactive alerting, and incident response across systems.
🟧 3. Terraform
Terraform is an open-source Infrastructure as Code (IaC) tool that lets you provision and manage infrastructure declaratively.
It supports multiple providers like AWS, Azure, and GCP using reusable modules.
I use it to automate VPC, EKS, RDS, and IAM setup with version control, enabling reproducible environments.
It improves consistency, automation, and collaboration in cloud infrastructure management.
Perfect, Pavan 🔥 — this is exactly how to prepare for interviews at top companies (Glean, Razorpay, etc.). Below are ready-to-speak, structured definitions for the most commonly asked DevOps & SRE tools/concepts, all following the same professional pattern you can use in interviews.
⚙️ Foundational Concepts
🟢 DevOps
DevOps is a culture and set of practices that unify development and operations teams to deliver software faster and more reliably. It focuses on automation, continuous integration, continuous delivery, and monitoring across the software lifecycle. In real-world terms, DevOps enables frequent deployments, faster feedback loops, and stable infrastructure through tools like Jenkins, Terraform, and Kubernetes. It ensures collaboration, speed, and reliability in software delivery.
🔵 SRE (Site Reliability Engineering)
SRE is a discipline that applies software engineering principles to operations for creating scalable and reliable systems. It focuses on availability, latency, performance, efficiency, and incident response, using concepts like SLOs, SLIs, and error budgets. In practice, SREs build monitoring, alerting, automation, and disaster recovery to ensure production reliability. The goal is to achieve high uptime and predictable performance through engineering, not manual ops.
🟣 GitOps
GitOps is a modern deployment practice that uses Git as the single source of truth for infrastructure and application configurations. It automatically syncs your cluster or environment to match the state defined in Git using tools like ArgoCD or Flux. In real setups, any change (e.g., updating an image tag or replica count) is pushed to Git, and ArgoCD applies it to Kubernetes automatically. This ensures version-controlled, auditable, and automated deployments.
🧱 Infrastructure & Configuration
🟧 Ansible
Ansible is an open-source configuration management and automation tool that uses YAML-based playbooks to define tasks. It helps automate server configuration, application deployment, and patch management. I use it with SemaphoreUI to schedule SSL renewals, backups, and log rotations with dynamic parameters. It reduces manual effort and configuration drift across environments.
🟨 Terraform
Terraform is an Infrastructure as Code (IaC) tool that provisions and manages cloud resources declaratively. It supports multiple providers (AWS, Azure, GCP) and uses modules for reusable infrastructure patterns. I use it to automate VPCs, EKS clusters, RDS, and IAM roles, versioned through Git for collaboration. It ensures consistency, repeatability, and scalability in infrastructure provisioning.
🟦 Docker
Docker is a containerization platform that packages applications with their dependencies into lightweight, portable containers. It ensures apps run identically across environments — dev, test, and prod. I use Docker to build microservice images, test locally, and deploy via Kubernetes or Compose. It simplifies deployment, scalability, and dependency management.
🟩 Kubernetes
Kubernetes is an open-source container orchestration platform that automates deployment, scaling, and management of containerized applications. It ensures self-healing, rolling updates, and load balancing. I manage EKS clusters using Helm, ArgoCD, and Karpenter for autoscaling. Kubernetes ensures reliability, scalability, and efficient resource utilization.
📈 Monitoring & Observability
🟪 Prometheus
Prometheus is an open-source monitoring and alerting tool for collecting and querying time-series metrics. It uses a pull model and integrates with exporters like node-exporter and kube-state-metrics. I use it with Grafana for dashboards, Loki for logs, and Alertmanager for alerts. It provides real-time visibility and proactive incident detection.
🟩 Grafana
Grafana is an open-source visualization and dashboard tool used to analyze and correlate metrics, logs, and traces. It connects with Prometheus, Loki, and Elasticsearch to visualize system performance. I use Grafana to build dashboards for CPU, memory, disk, and API latency monitoring. It enhances observability and decision-making through data visualization.
🟦 Loki
Loki is a log aggregation system from Grafana Labs designed for scalable and cost-efficient log storage. Unlike ELK, it only indexes metadata (labels), making it lightweight. I use it with Promtail for log collection and Grafana for visualization. It helps correlate logs with metrics for faster debugging.
🚀 CI/CD and Automation
🟧 Jenkins
Jenkins is an open-source automation server used to build, test, and deploy code in a CI/CD pipeline. It supports plugins, parallel builds, and pipeline-as-code. I’ve used GitHub Actions more, but Jenkins is ideal for complex, multi-step build processes. It automates continuous integration and delivery.
🟩 GitHub Actions
GitHub Actions is a CI/CD automation platform integrated with GitHub repositories. It allows workflows to run on push, PR, or scheduled triggers for build, test, and deploy automation. I use it to deploy Dockerized services to EKS, run linting and tests, and trigger ArgoCD sync automatically. It simplifies automation directly within source control.
🟦 ArgoCD
ArgoCD is a GitOps-based continuous delivery tool for Kubernetes. It continuously monitors Git repositories and applies manifests to clusters to keep the desired and live state in sync. I use it for auto-deployments, image updates, and rollback handling with Argo Image Updater. It ensures automated, auditable, and consistent deployments.
☁️ Cloud & Reliability
☁️ AWS
AWS is a cloud service platform that provides on-demand compute, storage, and networking. I work with EC2, EKS, S3, CloudWatch, IAM, and VPC to build and manage scalable infrastructure. AWS enables elastic scaling, high availability, and cost optimization through features like spot instances and auto-scaling groups.
🔵 Cloudflare
Cloudflare is a global CDN and security platform that accelerates and protects websites. It provides DNS, caching, WAF, DDoS protection, and Cloudflare Tunnel to securely expose services. I use it for SSL management, access control, and global traffic optimization. It enhances performance, security, and reliability at the edge.
Would you like me to continue this list with databases, caching, and message queue tools (like Redis, RabbitMQ, Kafka, etc.) next — in the same short interview-ready format? It’ll complete your “All DevOps Tools in 1 Page” sheet.
Nice catch, Pavan — thanks for the pointer. Here are interview-ready, template-based definitions for the missing items: Ansible, Bash, Python, SLA, SLO, SLI, and Git. Use the same short → extended → real-world pattern when answering.
🟧 Ansible
Short: Ansible is an open-source configuration management and automation tool that uses YAML-based playbooks. Core: It automates tasks like provisioning, application deployment, and configuration management by connecting over SSH (agentless). Real-world: I use Ansible to standardize server configuration, deploy application packages, and orchestrate maintenance tasks (e.g., SSL renewals, backups) across fleets. It reduces configuration drift and simplifies repeatable ops.
🐚 Bash
Short: Bash is a widely used Unix shell and scripting language for automating command-line tasks. Core: It provides shell primitives (variables, loops, conditionals) to write scripts that manage files, processes, and system tasks. Real-world: I use Bash for lightweight automation: startup scripts, scheduled cron jobs (swap creation, partition resize), log rotation, and quick operational tooling where a full program isn’t necessary.
🐍 Python
Short: Python is a high-level general-purpose programming language with strong ecosystem support for automation and web services. Core: It’s used for scripting, API clients, web services (FastAPI), and cloud automation (boto3 for AWS). Real-world: I build automation scripts, small APIs (FastAPI), and cloud orchestration tools using Python + boto3 — for example, snapshot automation, dynamic provisioning, or custom CI/CD helpers.
📄 SLA (Service Level Agreement)
Short: SLA is a formal contractual commitment between a service provider and a customer specifying expected service levels. Core: It defines penalties or remediation when agreed service levels (uptime, response time) are not met. Real-world: In enterprise offerings, SLAs are negotiated (e.g., 99.9% uptime), and we design architecture (redundancy, failover) and runbooks to meet these contractual obligations.
🎯 SLO (Service Level Objective)
Short: SLO is an internal targeted objective for service reliability, expressed as a percentage over time (e.g., 99.9% availability). Core: It’s derived from business requirements and helps define acceptable reliability without being a legal contract. Real-world: We set SLOs (latency, error rate) to guide engineering priorities — when error budget is exhausted, we limit feature rollout and prioritize stability work.
📈 SLI (Service Level Indicator)
Short: SLI is a measurable metric that indicates service performance (e.g., request latency, success rate). Core: SLIs feed into SLO calculations — they are the raw signals we monitor (p99 latency, availability). Real-world: Common SLIs: request success rate, request latency (p95/p99), and CPU saturation. We collect SLIs via Prometheus and use them to compute SLO compliance and trigger alerts.
🌳 Git
Short: Git is a distributed version control system for tracking code and configuration changes. Core: It enables branching/merging, history, and collaboration workflows (PRs, code review). Real-world: I use Git for IaC (Terraform modules), application code, and GitOps (ArgoCD). Git provides auditability and rollback capability — changes to infra or apps are versioned and reviewable.
If you want, I can:
Bundle these into a one-page “cheat sheet” PDF you can print or share, or
Expand each into a 2–3 sentence spoken version you can practice for interviews. Which would you prefer?
Startup and devops confidential
Business - Ecom
Items - decoration products
Customer - diwali, ganpati, navratri, wedding, birthday, others
Platform - shopify, social media, website
Shipping - Zapconnect
Analytics dahsboard - (sales, customer, inventary) via Apche superset
Technical
Seasonal selling - need kuberntes for scalling
apache superset for dahboards
clickhouse for quick retrival
ai agent for automation
security
python , rust for api
frontend react
cicd - github action and argocd
cloud - aws
database = rds
support botRAG for efficiency
Kubernetes
Kubernetes
cluster upgrades.
deploying helm charts
↳ monitor cluster Events
setting up alerts
troubleshooting Incidents & making sop
highly available app karpente + HPA.
اRBCA (role, binding, service Account)
setting up ingress, roting rules, ALB
creating manifest files (deployments, services, Daemon sets, state full sets, service account, HPA, probes)
secrets, configmap, vault
docker
task
dockerize app, make it lighter, secure
tagging it
maintaining ECR (lifecyce policy)
docker volume
docker network
docker architecture
docker compose
aws
tasks
EKS - kubernetes - managed nodegroup m ebs sorage, vpc cni ECR
RDS - database (replication, upgrade, snapshots backup, logs)
VPC - networking (peering, nat, IGW, subnets, ip devides)
IAM - (user, role, policy)
SG & NACL (WAF at instance and subnet level)
ASG and ALB (launch template - ec2, scalling policy, load balancing)
AWS lambda - event driven triger via Cloudwatch event and SNS
Cloudwatch - agent on ec2 to collect logs and store in log group, cloudwatch event
Cloudformation - Iac , easy rollback
Route53 - dns management
S3 - various storage types
cloudfront - for cachind and CDN
ACM - for ssl - use ARN in ingress resource
SES - for smtp configuration
cloudtrail - for audition
linux
Tsks
nginx proxy and certbot ssl
managing srvices, process, pid
bash script -ssl renew, create user
log monitoring and rotate logs
managae files and permissions
CPU, memory, disk tasks
networking task (PID, IP, port usage, free)
cicd
terraform
ansible
python
bash
powershell
monitoring (loki, prometheus, grafana)
Taks
monitor
application logs
k8s events
data piepline events
controller events - k8s scalling events karpenter
system metics
DNS healthcheck
database monitoring
alerting (grafana, alertmanager)
tasks
alert
bad request (via grafana and logql query)
k8s error events (via grafana and logql query)
controler events - pod scales, node scales - controller expose metrics - set alertmanager
CPU, DISK, Memory alert - alertmanager
DNS healthcheck failed, ssl expired, - alertmanager
token expired - grafana
pipeline job failed -
slow db query
AI agents
tasks
Integrated Groq Chat Model (LLM) with n8n AI Agent node to provide intelligent responses and decision-making for WhatsApp queries.
AI whatsapp chatbot ai agent using n8n
ETL/data - Postgres, airbyte, clickhouse
Reports - Apache superset
common production issues
issues
🚨 Production Issues Every DevOps Engineer Faces (and How We Solve Them) 🚨
🔥 1. High CPU / Memory Spikes
Symptoms: Pods or EC2s hitting 90–100% CPU, applications slowing down.
Root Causes: Memory leaks, unoptimized DB queries, infinite loops, traffic bursts.
How We Solve It:
Use monitoring tools (Prometheus, Grafana, CloudWatch).
Identify heavy processes (kubectl top pods, htop).
Restart or scale services, then work with devs to fix memory leaks.
Add auto-scaling rules for resilience.
🌐 2. DNS / Load Balancer Misconfigurations
Symptoms: Services healthy but users still see downtime.
Root Causes: Incorrect DNS TTL, failed health checks, misconfigured ALB/NGINX.
How We Solve It:
Validate DNS resolution (dig, nslookup).
Rollback recent LB/DNS changes.
Fix health checks, shorten TTL for faster recovery.
📦 3. Deployment Failures / Broken Releases
Symptoms: New release goes live, app immediately crashes.
Root Causes: Missing environment variables, broken Docker image, dependency mismatches.
How We Solve It:
Rollback instantly with Helm/K8s.
Debug container logs (kubectl logs).
Use blue-green or canary deployments to limit blast radius.
Automate pre-deployment checks.
🔑 4. Secret & Credential Leaks
Symptoms: Keys in GitHub or logs, causing potential security breaches.
Root Causes: Poor secret management practices.
How We Solve It:
Rotate leaked credentials immediately.
Use Vault / AWS Secrets Manager / SSM Parameter Store.
Integrate tools like trufflehog, git-secrets into pipelines.
📉 5. Database Performance & Connection Issues
Symptoms: “Too many connections”, query latency spikes.
Root Causes: Unoptimized queries, missing indexes, poor connection pooling.
How We Solve It:
Monitor DB metrics.
Increase connection limits, add caching (Redis).
Scale DB vertically/horizontally.
Optimize queries with dev team.
⚡ 6. CI/CD Pipeline Failures
Symptoms: Builds suddenly failing, blocking deployments.
Root Causes: Dependency updates, misconfigurations, low runner resources.
How We Solve It:
Debug logs and retry.
Clear build caches.
Add notifications (Slack/Teams).
Improve pipeline observability & resilience.
🔒 7. Security Vulnerabilities in Production
Symptoms: Scans reveal critical CVEs in containers or base images.
Root Causes: Outdated base images, missing patches.
How We Solve It:
Patch/rebuild Docker images.
Use lightweight & secure base images (distroless, alpine).
Automate scanning with Snyk, Trivy, Aqua.
📡 8. Network Latency & Cross-Region Issues
Symptoms: Slow service-to-service communication.
Root Causes: Cross-region calls, misconfigured VPC peering, API throttling.
How We Solve It:
Deploy services in same region or use edge caching.
Implement VPC endpoints/PrivateLink.
Add retries and circuit breakers for reliability.
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