"DevOps is a home, not a monument."

AI Platform Engineering Leadership Roadmap

My long-term career goal is to evolve from DevOps and platform engineering into an AI Platform Engineering / AI Infrastructure leadership role similar to organizations like Adobe Express, OpenAI, Anthropic, or Microsoft AI.

This roadmap focuses on:

• AI platform engineering
• LLM orchestration
• AI infrastructure
• AI developer enablement
• AI governance
• AI adoption at scale
• Engineering leadership

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Target Role

Areas of Responsibility

• Lead and grow engineering teams
• Drive AI platform architecture
• Build scalable AI systems
• Enable AI adoption across organizations
• Design AI infrastructure and runtime platforms
• Partner with product, research, and engineering teams
• Build production-ready LLM applications
• Improve developer productivity using AI
• Create governance and evaluation standards

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1. LLM Engineering

Topics to Learn

• Prompt engineering
• Tool calling
• RAG architectures
• AI agents
• Context engineering
• Memory systems
• Multi-agent orchestration
• AI evaluation frameworks
• Model routing
• Cost optimization

Resources

• OpenAI Cookbook
  https://cookbook.openai.com/

• Anthropic Engineering Blog
  https://www.anthropic.com/engineering

• LangChain Docs
  https://python.langchain.com/docs/introduction/

• LangGraph Docs
  https://langchain-ai.github.io/langgraph/

• LlamaIndex Docs
  https://docs.llamaindex.ai/

• Microsoft AI Agents for Beginners
  https://github.com/microsoft/ai-agents-for-beginners

• Prompt Engineering Guide
  https://www.promptingguide.ai/

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2. Agentic AI Systems

Topics to Learn

• Single-agent systems
• Multi-agent systems
• Agent orchestration
• Planning and reasoning
• Tool routing
• Memory management
• Retry and fallback systems
• MCP (Model Context Protocol)

Resources

• CrewAI Docs
  https://docs.crewai.com/

• AutoGen Docs
  https://microsoft.github.io/autogen/stable/

• Semantic Kernel
  https://learn.microsoft.com/en-us/semantic-kernel/overview/

• OpenAI Agents SDK
  https://openai.github.io/openai-agents-python/

• Ultimate LLM Agent Build Guide
  https://www.vellum.ai/blog/the-ultimate-llm-agent-build-guide

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3. RAG & AI Search Systems

Topics to Learn

• Embeddings
• Chunking strategies
• Vector databases
• Hybrid search
• Re-ranking
• Retrieval pipelines
• Evaluation strategies

Resources

• RAGAS Paper
  https://arxiv.org/abs/2309.15217

• NVIDIA RAG Course
  https://courses.nvidia.com/courses/course-v1:DLI+S-FX-15+V1/

• Pinecone Learn
  https://www.pinecone.io/learn/

• Weaviate Academy
  https://academy.weaviate.io/

• Qdrant Documentation
  https://qdrant.tech/documentation/

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4. AI Evaluation & Observability

Topics to Learn

• Hallucination detection
• Prompt versioning
• Regression testing
• AI tracing
• Latency tracking
• Cost monitoring
• Safety evaluation
• LLM benchmarking

Resources

• LangSmith
  https://docs.smith.langchain.com/

• Langfuse
  https://langfuse.com/docs

• Weights & Biases Weave
  https://weave-docs.wandb.ai/

• Maxim AI Blog
  https://www.getmaxim.ai/articles/8-best-prompt-engineering-tools-for-ai-teams-in-2025/

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5. AI Infrastructure & Platform Engineering

Topics to Learn

• GPU orchestration
• AI workload scheduling
• Distributed inference
• AI API gateways
• Model serving
• AI runtime platforms
• Kubernetes for AI
• Inference optimization

Resources

• KServe
  https://kserve.github.io/website/

• vLLM
  https://docs.vllm.ai/

• Ray Serve
  https://docs.ray.io/en/latest/serve/index.html

• NVIDIA Triton
  https://developer.nvidia.com/triton-inference-server

• Kubeflow
  https://www.kubeflow.org/

• BentoML
  https://docs.bentoml.com/

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6. AI Leadership & Governance

Topics to Learn

• AI governance
• Organizational AI adoption
• Secure AI systems
• AI rollout strategies
• AI compliance
• AI developer enablement
• AI success metrics

Resources

• Microsoft AI Architecture Center
  https://learn.microsoft.com/en-us/azure/architecture/ai-ml/

• AWS Generative AI
  https://aws.amazon.com/generative-ai/

• Google Research Publications
  https://research.google/pubs/

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7. Books to Read

Recommended Books

• Designing Data-Intensive Applications
• Building Machine Learning Powered Applications
• AI Engineering
• The LLM Engineering Handbook

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8. Portfolio Projects

1. AI DevOps Assistant

An AI agent that:

• Reads Kubernetes incidents
• Analyzes logs
• Queries observability tools
• Suggests remediation
• Creates incident reports

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2. Internal Enterprise RAG Platform

Features:

• Document ingestion
• Vector database
• Access control
• Evaluation pipelines
• Multi-model routing
• Tracing and observability

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3. AI Testing Framework for Microservices

Features:

• Contract test generation
• AI-powered API validation
• Test failure summarization
• Pact integration
• Kafka event validation

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4. Kubernetes AI Operator

Features:

• AI-driven autoscaling
• Cost optimization
• Intelligent remediation
• Runtime recommendations

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9. My Technical Focus Areas

Focus More On

• AI system design
• LLM orchestration
• AI runtime platforms
• Evaluation systems
• Context engineering
• AI governance
• AI adoption strategies
• Engineering leadership

Focus Less On

• Pure infrastructure automation
• Traditional CI/CD-only workflows
• Operations-only responsibilities

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10. Ideal Future Roles

• AI Platform Engineer
• AI Infrastructure Engineer
• AI Systems Architect
• AI Developer Experience Engineer
• AI Enablement Lead
• AI Runtime Platform Lead
• AI Engineering Manager
• AI Platform Engineering Leader

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Personal Positioning

My background in:

• Kubernetes
• DevOps
• Platform engineering
• Developer enablement
• Testing systems
• Architecture

creates a strong foundation for building scalable AI infrastructure and enabling AI adoption across engineering organizations.

The goal is to bridge:

• AI systems
• platform engineering
• developer productivity
• scalable infrastructure
• organizational AI transformation

into a leadership role focused on production-grade AI platforms.

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Docker, Kubernetes & Go — Hands-on Notes

Golang, Docker and Kube Practice session

Kubernetes 1.6+

Quotes to spice my work

“Innovation is taking two things that already exist and putting them together in a new way.”

• Tom Freston

“What's measured improves” ― Peter Drucker

“It's not about your resources, it's about your resourcefulness .”

• Tony Robbins

"Upon a falling card, birds soar high; Even paper learns to fly. But when the card rests on the ground, Only truth remains around."

• kalaignar

https://en.wikipedia.org/wiki/Peter_Drucker

Red Green Refactor https://quii.gitbook.io/learn-go-with-tests/

Learn -> adapt -> document -> share

Docker Desktop alternate

https://github.com/abiosoft/colima

## colima start --arch x86_64 --vm-type=qemu --cpu 8 --memory 16 --disk 100 --kubernetes
# To start colima with Kubernetes with x86_64 architecture
colima start
docker build . && docker ps -a 
colima stop

Helm

brew install helm

Automated PR

brew install github/gh/gh
git add .
git commit -am "just testing"
gh pr create -f

Go WorkSpace

Go1.18 feature of Go Workspace is enabled here.

cd ~/code/Devops
cd ..
go work init ./Devops (Note go.wrk file will be created, and ENV variable was assigned)
go work sync

Create Nginx Service

helm repo add ingress-nginx https://kubernetes.github.io/ingress-nginx
helm repo update

https://github.com/kubernetes/ingress-nginx/tree/master/charts/ingress-nginx

helm install -f minikube/nginx/values.yaml nginx ingress-nginx/ingress-nginx

$ minikube service ingress-nginx-controller  --url
http://192.168.99.100:32080
http://192.168.99.100:31443
http://192.168.99.100:32443

Add awesome-http.example.com in /etc/hosts to connect local

curl http://awesome-http.example.com/dev
curl http://awesome-http.example.com/dev/metrics

Kind environment

Install colima from previous steps, to run Kind we need docker engine is running.

colima start

Testing in kind cluster, port mapping required for docker image of Kube worker node. So please make sure extra port mappings are added in the kind/config.yaml Remember to add in /etc/hosts (to nginx to work)

Follow the document

https://github.com/ranjith-ka/Devops/tree/master/kind#kubernetes-in-docker-kind

CONTAINER ID        IMAGE                   COMMAND                  CREATED             STATUS              PORTS                       NAMES
89c1110261bb        kindest/node:v1.16.15   "/usr/local/bin/entr…"   13 minutes ago      Up 13 minutes       127.0.0.1:65273->6443/tcp   openfaas-control-plane
84a1f8bc9b54        kindest/node:v1.16.15   "/usr/local/bin/entr…"   13 minutes ago      Up 13 minutes       0.0.0.0:32080->32080/tcp    openfaas-worker

$ helm install -f minikube/dev/canary.yaml canary-dev charts/dev
$ helm install -f minikube/dev/prd.yaml prd-dev charts/dev

$ curl -s -H "testing: always" http://awesome-http.example.com/dev
Welcome to my canary website!%

$ curl -s -H "testing: never" http://awesome-http.example.com/dev
Welcome to my prod website!%

Install Cobra

Dadjoke CLI Tool

• Text tutorial: https://divrhino.com/articles/build-command-line-tool-go-cobra
• Video tutorial: https://www.youtube.com/watch?v=-tO7zSv80UY

Just trying out the tutorial

cobra init --pkg-name github.com/ranjith-ka/Devops
go mod init github.com/ranjith-ka/Devops

Add new command

cobra add random

Used below to convert JSON To go Struct online.

https://mholt.github.io/json-to-go/

Added the Plugin REST Client for postman things.

ctrl + alt + M -- Stop the running code.

Go Learning

https://github.com/StephenGrider/GoCasts

YAML remove comments

Remove all comments https://marketplace.visualstudio.com/items?itemName=plibither8.remove-comments

Mongo

To run mongo in local MAC, run the Make commands, this will be helpful for local testing. make run-mongo

Clean the logs, kill the process if not required.

GIT FLOW

I created GIT FLOW using the same nvie git flow, but added two release to understand better.

sequenceDiagram
    autonumber
    Alice->>John: Hello John, how are you?
    loop Healthcheck
        John->>John: Fight against hypochondria
    end
    Note right of John: Rational thoughts!
    John-->>Alice: Great!
    John->>Bob: How about you?
    Bob-->>John: Jolly good!

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Operators

https://developers.redhat.com/author/deepak-sharma

Application Usage Guide

This document provides instructions on how to use the application.

Prerequisites

1. Ensure you have Visual Studio Code installed.
2. Install the Copilot Chat extension from the VS Code marketplace.
3. Set up your development environment as per the project requirements.

Steps to Use the Application

1. Clone the repository:

   git clone <repository-url>
   cd <repository-folder>

2. Start the application:

   go run main.go serve

3. Open your browser and navigate to http://localhost:8080 to access the application.

4. Available endpoints:

   • /: Welcome message.
   • /hello: Displays the first HTTP program message.
   • /hello2: Displays the second HTTP program message.
   • /headers: Displays the request headers.
   • /joke: Fetches a random joke.

5. Open Visual Studio Code and navigate to the Copilot Chat panel.

6. Follow the instructions in the Readme to configure custom instructions.

Troubleshooting

• If you encounter issues, check the logs or refer to the FAQ section in this document.
• For further assistance, contact the support team.

Skaffold

   kubectl create secret generic kaniko-secret \
   --from-file=.dockerconfigjson=$HOME/.docker/config.json \
   --type=kubernetes.io/dockerconfigjson

## To activate the Profile with configs
skaffold dev -p prd

## Activate with module 
skaffold dev --module canary

AI CI/CD Platform

• Product blueprint: product/ai-cicd-platform/blueprint.md
• Spec-first API contract: specs/openapi.yaml
• Event contract: proto/ai_cicd_platform.proto
• Shared contracts: packages/contracts/README.md
• Product index: product/ai-cicd-platform/README.md
• Web scaffold: apps/web/README.md
• Inference scaffold: services/inference/README.md
• Local deployment guide: product/ai-cicd-platform/local-deployment.md