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Forward Deployed Engineer (FDE) Course

Forward Deployed Engineer (FDE) Course — Netsetos program hero, blueprint schematic style

Become the engineer companies send to their most important customers. The Forward Deployed Engineer (FDE) Course trains working software engineers for customer-facing AI delivery — scoping ambiguous client problems, building production GenAI systems fast, integrating with real-world data, and shipping under real constraints. What's inside: • 12 modules · 75+ hands-on steps covering discovery, solution design, rapid prototyping, RAG and agent delivery, deployment, and client communication • Runnable code, practice labs, and interview Q&A throughout • Live course sessions + Discord community support • Built and taught by a Technical Architect with 15+ years of client delivery experience Who it's for: engineers comfortable with Python who want to move into high-impact, customer-facing AI roles — one of the fastest-growing engineering tracks of 2026. Works best after the free GenAI Engineering course (or equivalent experience).

Course syllabus

  1. Module 1 - The FDE Mindset & the AI–Customer Gap
    1. 1.1 Origin of the FDE
    2. 1.2 Why AI labs copy the Palantir playbook (2024-2026)
    3. 1.3 FDE vs adjacent titles
    4. 1.4 Who's hiring FDEs
    5. 1.5 The senior- engineer pivot (+Interview Q&A)
  2. Module 2 - Production Python for FDEs
    1. 2.1 Modern Python Tooling
    2. 2.2 Async/await for concurrent LLM workloads
    3. 2.3 Pydantic v2 and structured outputs
    4. 2.4 Testing - pytest, hypothesis property-based, snapshot
    5. 2.5 Structured logging & OpenTelemetry
    6. 2.6 Packaging (+Q&A)
    7. 2.7 LLM API Engineering
  3. Module 3 - Prompt Engineering, Structured Outputs
    1. 3.1 Prompt anatomy
    2. 3.2 Few-shot / zero-shot / CoT / ReAct
    3. 3.3 OpenAI JSON mode vs Anthropic tool-use
    4. 3.4 Function calling & tool use
    5. 3.5 Prompt caching, batching, Cost engineering
    6. 3.6 Prompt Versioning ("prompt as code")
    7. 3.7 Prompt Injection - top 5 attacks and defences (+Q&A)
  4. Module 4 - RAG Architectures
    1. 4.1 Embeddings Models
    2. 4.2 Chunking Strategies
    3. 4.3 Hybrid Search and Reranking
    4. 4.4 Vector Stores Deep-Dive
    5. 4.5 Top RAG Failure Modes
    6. 4.6 Ragas as a CI gate (+Q&A)
    7. 4.7 GraphRAG, Agentic & Multimodal Retrieval
  5. Module 5 - Agent Frameworks & MCP
    1. 5.1 OpenAI Agents SDK
    2. 5.2 LangGraph (stateful workflows)
    3. 5.3 CrewAI & AutoGen v0.4
    4. 5.4 When raw SDK beats frameworks
    5. 5.5 MCP fundamentals
    6. 5.6 Building production MCP servers
    7. 5.7 Sub-agents & agent skills (+Q&A)
    8. 5.8 Browser Agents
    9. 5.9 Voice & Realtime Agents
  6. Module 6 - LLM Evaluation & Observability
    1. 6.1 Golden Datasets & SME workflow
    2. 6.2 Ragas Metrics in Depth
    3. 6.3 DeepEval (pytest-native gate)
    4. 6.4 Braintrust & LangSmith
    5. 6.5 Langfuse Self-hosted
    6. 6.6 LLM-as-judge
    7. 6.7 Customer Eval Scorecard (+Q&A)
  7. Module 7 - Vector DBs, Document Parsing
    1. 7.1 pgvector Deep-dive
    2. 7.2 Qdrant vs managed Pinecone
    3. 7.3 Document Parsing
    4. 7.4 OCR for Scanned Documents
    5. 7.5 ETL Patterns for SaaS Sources
    6. 7.6 OAuth 2.0 and webhooks
    7. 7.7 PII redaction before Model calls (+Q&A)
    8. 7.8 Synthetic Data Generation
  8. Module 8 - Deployment, Security & Cloud Realities
    1. 8.1 Dockerizing FastAPI + LLM services
    2. 8.2 Kubernetes Basics
    3. 8.3 Managed Model APIs
    4. 8.4 AI Options - Regional Providers
    5. 8.5 Cost Optimization at Architecture level
    6. 8.6 Enterprise Identity & Multi-tenancy
    7. 8.7 Security & ops : Secrets, IAM, VPC (+Q&A)
    8. 8.8 Fine-Tuning QLoRA
  9. Module 9 - Customer-Facing Skills: Discovery
    1. 9.1 Discovery frameworks - SPIN, JTBD
    2. 9.2 Stakeholder Mapping
    3. 9.3 Engagement Brief : Artifact
    4. 9.4 Demo design - 3 Act
    5. 9.5 Procurement & Security
    6. 9.6 Executive comms - Stripe/Amazon 6-pager Discipline
    7. 9.7 Hack In front of the Customer
    8. 9.8 Writing PRDs for AI Products (+Q&A)
  10. Module 10 - Compliance, Security & Regulation
    1. 10.1 Data Protection Laws : GDPR, CCPA, EU AI Act
    2. 10.2 DPIA for LLM Workloads
    3. 10.3 Sector Regulation : Financial Services
    4. 10.4 SOC 2 & ISO 27001
    5. 10.5 International Data Flows [Jurisdictional Customers]
    6. 10.6 PII Tokenization & Masking Patterns
    7. 10.7 Machine Unlearning & right to erasure for LLM (+Q&A)
    8. 10.8 LLM Audit Engagement
  11. Module 11 - Capstone: The Mock Customer Engagement
    1. 11.1 Capstone kickoff - Choose Your Track
    2. 11.2 Mid-sprint Review & Architecture Pivot
    3. 11.3 Architecture Review
    4. 11.4 Eval Review - RAGAS and DeepEval
    5. 11.5 Final Demo Prep (15-min Walkthrough)
    6. 11.6 Capstone Defense Session (+Q&A)
  12. Module 12 - Demo Day, Portfolio, Interview Prep
    1. 12.1 Demo
    2. 12.2 Portfolio Structure
    3. 12.3 Resume & LinkedIn for FDE Role
    4. 12.4 Interview Loop Simulation - 7 Stages of FDE
    5. 12.5 Negotiation Across Markets (+Q&A)

Questions about this course

What does it cost?

₹200, paid once. Our prices include 18% GST. Under our Refund Policy you can get a full refund within 7 calendar days of purchase if you have completed less than 20% of the lessons; after that the purchase is final.

How long does it take?

It is self-paced, so it depends on you. There are 85 steps across 12 modules, ending with a mock customer engagement capstone and a demo day module.

Is there a certificate?

No. This course doesn't award a certificate.

Is this a live course?

No. It is self-paced: you work through the 85 steps on your own schedule.

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