Future-Proofing: What to Learn Now

Ranked by (verified market signal × durability × your leverage). Each entry: skill → reasoning → concrete first action.

Tier 1 — do these regardless of which path you pick

1. Python — the +7pp/yr language ✅, prerequisite for every AI door. Action: Task 5 Stages 1–4; re-implement one of your Express APIs in FastAPI.
2. LLM API engineering (TS first, then Python) — 81.4% of devs already touch GPT models ✅; embedding AI is now as common as data processing ✅. Streaming, tool calling, structured output, token/cost budgeting. Action: ship one streaming chat feature behind your own Angular UI this month.
3. RAG + vector search — the standard pattern for “AI + company data,” core of most real 2025–26 postings [secondary]. Includes chunking, embeddings, pgvector/Pinecone, reranking. Action: “chat with my docs” portfolio app with citations.
4. Eval design — “single best signal of real LLM experience” in 2026 hiring guides [secondary]; separates demo-builders from engineers. Action: add a golden-set eval harness + regression gate to the RAG app.
5. Agentic patterns + MCP — the 2025→2030 growth curve ✅ (timeline verified); MCP is the emerging interop standard. Action: raw tool-calling loop, then one MCP server for MySQL.

Tier 2 — strong multipliers

6. AI-assisted development mastery (Cursor/Claude Code/Copilot) — use the METR lesson ✅: measured gains require skillful use; naive use made experts slower in early 2025. Being the person who knows when not to use the tool is a differentiator. Action: adopt one agentic tool for all side-project work; keep a personal win/loss log.
7. React (breadth) — Angular is stable-not-growing ✅; React ~2.5× the demand ✅. Two weeks of TS-based React unlocks most frontend postings. Action: one portfolio app in Next.js.
8. Security for AI systems — prompt injection, data exfiltration via tools, agent authZ. Your JWT/RBAC background makes this a cheap premium niche. Action: study OWASP Top 10 for LLM Applications; apply to your agent project.
9. Cloud + containers to competence (Docker, one cloud, basic K8s) — every AI deliverable ends in deployment. Action: containerize and deploy the RAG app.

Tier 3 — positioning, not tech

10. Domain depth + product judgment — WEF: 39% of skills churn by 2030 ✅; judgment compounds while syntax depreciates. Specialize in a domain (fintech/health/logistics) where India hiring is strong.
11. Public proof of work — GitHub portfolio + short write-ups. In an AI-flooded resume market, verifiable artifacts beat keyword lists. Action: every project above gets a README with metrics and a 5-line “what I measured” section.
12. English communication & remote-work polish — the verified 8× India↔US comp gap ✅ is the arbitrage; remote AI-skilled roles are the realistic path to global pay from India.

Anti-recommendations (evidence-based don’ts): don’t bet more years on Angular-only depth (flat share ✅); don’t chase standalone prompt-engineer roles (title absorbed); don’t start with a classical-ML/math track for employability (Task 7 #5 analysis); don’t skip evals/observability — that’s where demos die in production.

Layoff-risk logic (verified chain): seniors in AI-exposed roles grew +6–9% while juniors fell ✅ → your 5+ years is protection today; two-thirds of employers hire for AI skills ✅ → adding Tier 1 converts protection into premium; 39% skill churn by 2030 ✅ → the habit of continuous reskilling matters more than any single item on this list.