Free interactive career map · Updated Aug 2026

From DevOps to Production AI

One clear path — not a wall of boxes. Start at Linux and Python, follow six stages to Kubernetes, MLOps, RAG, agents, and GPU infrastructure. Pick a target role and the map greys out everything you do not need.

Every skill answers the same four questions: what it is, why it exists, what to build with it, and what breaks at 2am.

Find My AI PathCompare CareersRole Roadmaps
The path

Six stages, in order

Tap any skill to open it. Tick the box to track what you have finished — your progress stays in this browser.

Step 1 · Pick your destination

Full path

0 of 16 skills marked done

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Tick a skill to track it. Progress is saved in this browser only — nothing is uploaded.

1

Foundation

The ground everything else stands on. Skip this and nothing later makes sense.

2

Package & Automate

Make your work reproducible so a machine can build and ship it for you.

3

Run & Observe

Operate workloads at scale and know what they are doing in production.

4

ML Lifecycle

Track experiments, register models, and serve predictions behind an API.

5

LLMs & Retrieval

Ground language models in your own data instead of hoping they guess right.

6

Agents & Scale

Give models tools, then serve them fast and cheap on real GPUs.

Destination

Production AI Engineer

You can build it, ship it, watch it, and fix it at 2am. That is the whole job.

Skill library

Every skill, explained in depth

Each page covers what the technology is, why it exists, the topics to learn, a project to build, production failure modes, and interview talking points.

KubernetesContainer orchestration for production workloads including ML and LLM inference.DockerContainer packaging for reproducible ML and AI deployments.PythonPrimary language for ML, LLM apps, automation, and data engineering.MLflowExperiment tracking, model registry, and ML lifecycle management.RAGRetrieval-augmented generation for enterprise knowledge applications.Vector DatabasesStores and queries embeddings for semantic search and RAG.LangChainFramework for LLM applications, chains, and agent orchestration.MCPModel Context Protocol for connecting agents to enterprise tools and data.LLMsLarge language models — APIs, prompting, fine-tuning, and evaluation.vLLMHigh-throughput LLM inference serving with PagedAttention.GPU InfrastructureGPU scheduling, memory, and compute for AI training and inference.TerraformInfrastructure as code for reproducible cloud and AI platforms.PrometheusMetrics collection and alerting for ML and AI systems.KServeKubernetes-native model serving with autoscaling and canary deployments.LinuxOperating system fundamentals for all infrastructure and AI engineering.CI/CDAutomated build, test, and deploy pipelines for ML and software.
Destinations

Where this path can take you

DevOps Engineer

CI/CD, containers, infrastructure automation, and release engineering.

₹10–35 LPA

MLOps Engineer

Owns the ML lifecycle from experiment to production deployment and monitoring.

₹12–40 LPA

View roadmap →

LLMOps Engineer

Operationalizes LLMs — RAG, evaluation, cost control, and production serving.

₹18–50 LPA

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AI Infrastructure Engineer

GPU clusters, inference runtimes, and compute infrastructure for AI at scale.

₹15–40 LPA

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Forward Deployed Engineer

Customer-facing AI delivery combining engineering, integration, and solution design.

₹18–45 LPA

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AI Engineer

Builds LLM applications, agents, and production AI features.

₹15–50 LPA

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Want someone to walk this path with you?

The live cohorts follow this exact map — hands-on labs, capstone projects, and 1-on-1 mentorship instead of watching videos alone.

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