Free interactive career map · Updated Aug 2026

From DevOps to Production AI

Eight stages, 36 market-critical skills — from Linux and Git through Kubeflow, Spark, LangGraph, and GPU inference. Pick a target role and grey out what 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.

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The path

Eight 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

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

1

Foundation

OS, version control, Python, data, networking, and APIs — assumed in every job posting.

2

Package & Automate

Containers, CI/CD, infrastructure-as-code, and a real cloud account (AWS / GCP / Azure).

3

Run & Observe

Kubernetes, Helm, and the Prometheus + Grafana stack teams actually run in production.

4

ML Engineering

Train with PyTorch & HuggingFace, track with MLflow, version data with DVC.

5

ML Production

Ship models behind FastAPI, serve with KServe, monitor drift, manage features.

6

LLMs & RAG

LLM APIs, retrieval, vector DBs, evaluation, guardrails, and AI security.

7

Agents & Orchestration

LangChain, LangGraph, MCP — how production agent systems are wired.

8

Scale & Platform

GPU inference, vLLM, Kubeflow pipelines, Spark data, Redis caching.

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.GitVersion control for code, configs, and ML artifacts — required in every engineering role.SQLQuery and model structured data — features, metrics, and app state all live here.Cloud (AWS / GCP / Azure)Managed compute, storage, and AI services — where production workloads actually run.HelmPackage and release Kubernetes apps — standard for MLOps and platform teams.GrafanaDashboards and alerting on top of Prometheus — how teams actually see production health.FastAPIProduction Python APIs for ML inference, RAG endpoints, and agent backends.PyTorchTrain and fine-tune models — the ML foundation behind MLOps and many LLM workflows.NetworkingTCP/IP, DNS, load balancers, and VPCs — debug latency and outages in AI services.REST APIsHTTP APIs that connect frontends, agents, and model services.HuggingFacePre-trained models, tokenizers, and the Transformers ecosystem.DVCGit for data and models — version datasets and pipeline stages.Feature StoresServe consistent ML features in training and production inference.Drift DetectionDetect when production data or model performance degrades.LLM EvaluationMeasure RAG and LLM quality before and after shipping.GuardrailsInput/output filters, PII redaction, and policy enforcement for LLMs.AI SecurityThreat modeling, secrets, RBAC, and compliance for AI systems.LangGraphStateful multi-step agent workflows with cycles and human-in-the-loop.KubeflowEnd-to-end ML pipelines on Kubernetes — training, tuning, serving.Apache SparkDistributed data processing for large-scale feature engineering and training data.RedisIn-memory cache for sessions, rate limits, and low-latency feature serving.
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

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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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