The AI industry moves fast, but breaking in requires more than just following tutorials. This 12-week roadmap combines industry-proven patterns from companies like Palantir, Google, and Anthropic with hands-on projects that build real expertise. Whether you’re transitioning into AI or deepening your technical foundation, this plan balances learning fundamentals with shipping production-ready systems.

Each week targets specific skills employers actually need: from RAG evaluation and agent orchestration to cost optimization and security patterns. The timeline assumes ~1 hour daily commitment, with built-in feedback loops to validate your progress against industry standards.

12-Week AI Engineering Roadmap #

Week 1 — RAG foundations, end-to-end

• Learn: DeepLearning.AI’s RAG course (architecture → deployment → evaluation). (DeepLearning.ai)
• Build: A minimal RAG API (your project from before) + README diagram.
• Optional primer: OpenAI Cookbook overview patterns. (OpenAI Cookbook)

Week 2 — Evaluating RAG like a pro

• Learn: DeepLearning.AI “Building & Evaluating Advanced RAG” (context relevance, groundedness, answer relevance). (DeepLearning.ai)
• Build: An eval harness (accuracy, groundedness, latency) and publish a metrics table. (DeepLearning.AI - Learning Platform)

Week 3 — Ontology & operational modeling (Palantir way)

• Learn: Palantir Ontology & AIP overview—objects, links, actions; how the ontology becomes a “digital twin.” (Palantir)
• Build: A YAML “mini-ontology” for your domain (entities, relations, roles, actions) + example action I/O.
• Deep dive (optional): OSDK concept post. (Palantir Blog)

Week 4 — Agents 101 (open-source + managed)

• Learn: LangGraph docs (stateful agent loops & control). (LangChain)
• Build: One tool-using agent (plan → retrieve → act → verify) for your app.
• Try managed: Vertex AI Agent Builder codelab to see enterprise agent patterns. (Google Codelabs)

Week 5 — Systematic evals (tools, structured outputs)

• Learn: OpenAI Evals framework + tool-use evaluation examples. (GitHub)
• Build: Nightly eval run (GitHub Action) grading your agent’s tool calls on a golden set.

Week 6 — Tool-use ergonomics (Anthropic) & MCP

• Learn: Claude tool-use guides (design good tool schemas; latency & streaming tips). (Claude Docs)
• Learn+: Model Context Protocol (standard way to wire apps/tools to LLMs). (Claude Docs)
• Build: Refactor your tools with clearer descriptions & safer I/O; add one MCP-style connector.

Week 7 — Reliability & security basics

• Learn: Google Cloud AI/ML architecture best practices (grounding, security, pipelines). (Google Cloud)
• Build: Add request IDs, retries, timeouts, PII scrubbing on ingest, and role-based visibility.

Week 8 — Production polish

• Learn: Vertex AI docs for deployment patterns & monitoring. (Google Cloud)
• Build: p50/p95 latency dashboard, cost per request, and a failure-modes section in your runbook.

Week 9 — Advanced agents & orchestration

• Learn: LangGraph v1.0 alpha notes (durable execution & fine-grained control). (LangChain Blog)
• Build: A small multi-agent workflow (e.g., triage-agent → completion-agent → verifier).

Week 10 — “Actions” that change the real world

• Learn: Palantir AIP Logic (LLM functions tied to ontology actions). (Palantir)
• Build: Ship at least one safe, auditable write-action (e.g., calendar, ticket, email draft with human-in-the-loop).

Week 11 — Cloud deployment & cost envelope

• Learn: Vertex Agent Builder overview (components, datastore grounding). (Google Cloud)
• Build: Deployed pilot (auth, logs, budget alerts) + 1-page cost plan (<$50/mo pilot).

Week 12 — Hiring signal packaging

• Align with Palantir’s FDSE/forward-deployed profile (outcome-focused, customer-facing). (Lever)
• Ship: Case study (2 pages with metrics), 5-min demo script, résumé bullets tied to evals/SLAs.

Weekly cadence (fits ~1 hr/day) #

• Mon Build (45m) + README log (15m)
• Tue Data/Ontology tweak + update evals
• Wed Operator loop (talk to 1 user; add 1 task to golden set)
• Thu Reliability (tests/guardrails)
• Fri Polish (UI & runbook)

Optional “theory backbone” #

• Stanford CS224N (NLP deep learning; up-to-date videos). (Stanford University)
• Berkeley LLM Agents (agent reasoning & advanced topics). (rdi.berkeley.edu)
• MIT 6.5940 EfficientML (deploy/optimize under constraints). (MIT EECS)