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Very High demand Excellent for remote

AI Engineer roadmap

The hottest track of 2026 — build production LLM apps, RAG systems, and agents.

Every company is now an AI company. AI Engineers — people who ship LLM features, not just train models — are the scarcest hire in tech. Remote-first AI startups pay top-of-market because the talent pool is tiny.

What it pays

India: ₹12–25 LPA entry → ₹40–80 LPA experienced

Remote worldwide: $130k–$250k remote

Before you start

  • Python fluency
  • REST APIs
  • Basic ML intuition (what a model/embedding is)

The plan, phase by phase

1

Python for AI + LLM fundamentals

Weeks 1–6

Skills

Advanced Python (async, typing)How transformers & tokens work (intuition, not math)Prompt engineering that actually worksEmbeddings & vector search concepts

Learn it free

Build: A prompt playground: compare models/prompts on a real task and document what changed output quality.

Interview angle: 'How do tokens work? Why do LLMs hallucinate?' — they test mental models, not trivia.

2

RAG systems that work in production

Weeks 7–14

Skills

Chunking strategiesVector DBs (pgvector, Pinecone, Qdrant)Hybrid search (dense + BM25)Evaluation: faithfulness, groundednessHandling hallucinations in UX

Learn it free

Build: RAG over a real document set (e.g. company docs) with an eval harness measuring answer faithfulness.

Interview angle: Everyone claims RAG experience; few can discuss chunking tradeoffs and eval metrics. Be the few.

3

Agents + shipping LLM apps

Weeks 15–22

Skills

Tool calling / function callingAgent loops and planningLangGraph / raw orchestrationStreaming UX, latency budgetsCost control (caching, model routing)Evals as CI

Learn it free

Build: An agent that does a real multi-step job (research assistant, code reviewer) with evals and a cost dashboard.

Interview angle: 'Design an agent for X' — they want workflow decomposition and failure-mode thinking.

4

Go public, get hired

Weeks 23–28

Skills

Writing about what you built (blog/Twitter)Open-source AI contributionsSystem design for LLM appsTake-home project excellence

Learn it free

Build: Ship 1 AI app people actually use + write 2 deep technical posts about what you learned.

Interview angle: AI startups hire through proof of work. Your shipped app + writing beats a certificate every time.

Remote-readiness checklist

  • Shipped LLM app with real users (even 10 users counts)
  • Can explain RAG eval metrics without hand-waving
  • Writes clearly about technical tradeoffs (blog or README)
  • Follows the AI engineering conversation (papers, talks, changelogs)

Portfolio proof you need

  • 1 production RAG system with evals
  • 1 agent with cost/latency dashboard
  • 2 technical write-ups
  • Hugging Face / GitHub presence

Roadmap done? Now get the job.

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