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Forward Deployed Engineer Roadmap

You embed with customers and ship production AI in their environment

The Palantir-invented role that OpenAI, Anthropic, and Google are now hiring at scale (2026). $160K-$400K US, $35L-$1.5Cr+ India remote at global labs. 1,165% YoY posting growth.

The route

  1. Week 1 - Forward Deployment Engineering: Identity first. You learn what the job is, how it differs from SWE, and the salary/geography map in week 1 - then the role map threads through the whole path: discovery, demo craft and the full-stack surface in week 3, eval-first in week 5, MCP servers in week 7, VPC deployment in week 12, the field reality in week 13, and the graded portfolio artifact plus the 7-day engagement simulation in weeks 15-16
  2. Week 1 - Python for ML: Production Python is non-negotiable - you will write integration code in customer environments from day one
  3. Week 1 - SQL for Data: Coding rounds test joins on messy enterprise tables; you will query customer warehouses constantly
  4. Week 2 - AI Tools: Two short modules, deliberately placed early. Agentic coding tools are how an FDE ships a walking skeleton inside a customer environment in days rather than weeks, so the payoff compounds across every week that follows; learned at the end it is a footnote
  5. Week 2 - Git & Version Control: PR etiquette inside customer codebases - branching, conflicts, reviews on someone else's repo
  6. Week 2 - Jupyter & Colab: On-site demos and quick exploration during scoping workshops
  7. Week 3 - LLM: LLM API + RAG + prompt engineering appear in 31-46% of FDE postings - the core of every customer engagement. Trimmed of pre-training, RLHF, MoE, distillation and scaling laws: an FDE never trains a model, so that is frontier-lab research content, not job content
  8. Week 4 - Foundations: Read 1e-4, 7B, 128K and Sigma/argmax without stalling. Vocabulary for customer conversations, nothing more
  9. Week 4 - Linear Algebra: Two modules only: a vector is a list of numbers with a direction, and the dot product is why similarity search returns what it returns. That is the floor for explaining an embedding to a customer. No eigenvalues, no SVD - an FDE derives nothing
  10. Week 4 - Statistics: You reason about eval metrics and A/B tests, you do not derive them - aim for fluency, not depth
  11. Week 4 - ML Algorithms: Customers ask "why not classical ML for this" - answer credibly without re-learning the field
  12. Week 4 - Deep Learning: Transformer literacy enough to debug a fine-tune or explain attention to a CTO, not train from scratch
  13. Week 5 - Software Engineering for AI: Arrives in two slices. Evaluation engineering lands in week 5, before agents, because you build the judge before the thing it judges. The production-code cluster - testing, API + system design, code review, observability and cost - follows in week 8 and is what separates the pilots that ship from the ones that stall. Skips the inference-infra cluster (engine internals, GPU scheduling, LoRA serving): that is ML-platform specialist work, not FDE work
  14. Week 6 - Agentic AI & Multi-Agent Systems: Google FDE = "Agent Engineer"; Anthropic asks "have you shipped agents into production" as a screen question. Trimmed of framework-of-the-month sprawl - know one orchestration framework deeply and the seams between them
  15. Week 9 - ML Pipeline: Customers ask "how does data flow through your system" - end-to-end mental model for serving and monitoring. Given its own week so that week 8 is not a 25-module wall
  16. Week 10 - MLOps & Deployment: Docker, Kubernetes, monitoring, CI/CD - the deployment baseline for any FDE. Moved ahead of VPC deployment: you cannot ship an air-gapped Kubernetes manifest before you have met Kubernetes. Trimmed of GPU-SKU selection and its own capstone, both of which belong to ML-platform specialists or compete with the two capstones this path already ends in
  17. Week 11 - AI on Cloud: AWS, GCP and Azure all appear across FDE postings - multi-cloud is table stakes; whatever the customer runs, you ship on
  18. Week 17 - Interview Q&A: FDE loops at Palantir, OpenAI, Anthropic, and Google are scenario-heavy - decomposition, client simulation, and behavioral rounds dominate. The required bank is the FDE one. Classical-ML, deep-learning-theory, math and implement-an-algorithm-from-scratch banks are excluded outright, and the general LLM, agent and ML-system-design banks sit on the optional shelf: they test model-builder depth at the wrong altitude for an embedded role
  19. Week 17 - AI System Design: FDE loops ask you to design a system live, in front of a client. The method here is the part that survives a whiteboard: the numbers first, then the architecture they force