LLM / AI engineering path: RAG, agents, MCP
The 2026 AI engineering path for Indian devs — RAG pipelines, agent frameworks, Anthropic MCP, evals. What to build, what to skip (fine-tuning), and which roles hire.
LLM / AI engineering path: RAG, agents, MCP
Status: Outline — full guide coming soon. Get notified when it ships →
What this covers
- The 4 skill layers — prompting, RAG, agents/tool-use, evals — and the order to learn them
- Why fine-tuning is the wrong starting point in 2026 (and when it isn't)
- The Anthropic MCP spec as a portfolio shortcut: build a real server, get inbound
- Which remote AI engineer JDs actually hire from India vs which gate on US presence
Why this matters for Indian devs going remote
AI engineering is the one role category in 2026 where supply is still meaningfully short of demand, and where a shipped portfolio project (a working RAG pipeline, a working MCP server) closes the gap to senior comp faster than any other path. The category is also new enough that "no prior AI experience" isn't a filter — your last 6 months of building counts.
Outline
The 4 skill layers, in order
TODO — prompting fundamentals, RAG, agents/tool-use, evals & observability. What to build at each layer.
Why fine-tuning is the wrong start
TODO — the 2026 frontier-model gap, infra cost, lack of eval discipline; when fine-tuning does matter (domain-specific narrow task).
MCP as a portfolio shortcut
TODO — Anthropic's spec is new, examples are sparse, a published server gets inbound.
Which remote AI roles hire from India
TODO — the JD signal patterns; full-time vs contract; US-only gatekeeping language to spot.
Common mistakes to avoid
- Starting with fine-tuning a 7B model on a Kaggle dataset (looks junior)
- Calling LangChain demos "production AI engineering" on your resume
- Skipping evals — every senior AI eng JD asks about them
Templates / examples
TODO — a learning-roadmap with weekly build targets, a starter MCP server scaffold, and a RAG evaluation harness template.
Related guides
- Building an MCP server as a portfolio weapon
- Learn-by-building tracks: build an MCP server, build a RAG pipeline
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