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AI Forward Deployed Engineer (FDE)

From technology engineer to customer-ready AI engineer.

Learn to take an enterprise AI problem from discovery and scoping to prototype, production and customer adoption. A practical, project-led program for software, full-stack, cloud, data, DevOps and technical engineers who want to move into high-impact AI delivery and Forward Deployed Engineering roles.

See the learning journey

Duration

6 to 8 weeks

Format

Hands-on, project-led

Target audience

3 to 8 years' experience

Focus

Enterprise AI delivery

Why become an FDE?

  • Work directly on real enterprise problems rather than only coding from predefined requirements.
  • Combine software engineering, GenAI, cloud, customer discovery and solution delivery.
  • Build prototypes quickly, make practical technology trade-offs and take solutions toward production.
  • Develop the communication and problem-solving skills needed to work with business and technology stakeholders.

6 to 8 week learning journey

Week 1

Jira · Confluence · process mapping

FDE mindset and problem discovery

Customer discovery, business problem framing, user journeys, AI use-case identification.

Week 2

Python · REST · JSON · APIs

Technical scoping and solution design

Requirements, MVP scope, feasibility, build-vs-buy, RAG-vs-fine-tuning, cost and latency trade-offs.

Week 3

OpenAI · Anthropic · Gemini · AWS Bedrock

LLM and GenAI engineering

Tokens, context, prompting, structured output, tool and function calling, model selection.

Week 4

LangChain · LlamaIndex · PostgreSQL/pgvector · OpenSearch

RAG and enterprise data

Chunking, embeddings, retrieval, vector search, reranking, grounding.

Week 5

LangGraph · MCP · REST APIs · Python

Agentic AI and integrations

Tokens, context, prompting, structured output, tool and function calling, model selection.

Week 6

FastAPI · React · Docker · AWS · GitHub Actions

Production engineering

Backend, UI, containers, CI/CD, cloud deployment, logging and error handling.

Week 7

Pytest · CloudWatch · OpenTelemetry

Evaluation, security and governance

AI evaluation, quality, latency, cost, prompt injection, PII, access control, monitoring.

Week 8

Full enterprise stack

FDE capstone and customer demo

Discovery, scope, build, evaluate, deploy, then demonstrate business value.

What you will learn, and what you will be able to do

Customer and business problem solving

  • Translate an ambiguous business challenge into a clear AI problem statement.
  • Identify users, stakeholders, data sources, constraints and measurable outcomes.
  • Determine whether AI, automation or a conventional software approach is the right solution.

AI and LLM engineering

  • Build practical LLM applications using commercial and cloud-hosted models.
  • Use prompting, structured outputs, embeddings, RAG and tool calling.
  • Design agent workflows with clear boundaries, escalation and human oversight.

Enterprise integration and production

  • Connect AI applications to APIs, databases, documents and enterprise systems.
  • Build production-style services with Python/FastAPI, React and Docker.
  • Deploy and operate solutions in AWS with CI/CD, logging and monitoring.

AI evaluation, security and governance

  • Define evaluation datasets and measure relevance, groundedness, accuracy, latency and cost.
  • Identify risks such as hallucination, prompt injection, data leakage and unauthorized access.
  • Apply practical controls for responsible and production-ready AI.

Technology stack

Programming and backend

Python

FastAPI

JSON

REST APIs
Pytest

Frontend

React
TypeScript

GenAI / LLM

OpenAI

Anthropic
Gemini
AWS Bedrock

RAG and AI frameworks

LangChain
LlamaIndex
LangGraph

Programming and backend

Python

FastAPI

JSON

REST APIs
Pytest

Frontend

React
TypeScript

GenAI / LLM

OpenAI

Anthropic

Gemini

AWS Bedrock

RAG and AI frameworks

LangChain

LlamaIndex

LangGraph

Agentic AI

Tool calling

Agent workflows

MCP

Human-in-the-loop

Data and retrieval

PostgreSQL
pgvector
OpenSearch
Embeddings

Cloud and DevOps

AWS
Docker

GitHub Actions

Terraform

Observability

CloudWatch
OpenTelemetry
AI evaluation and cost monitoring

Agentic AI

Tool calling
Agent workflows

MCP

Human-in-the-loop

Data and retrieval

PostgreSQL
pgvector
OpenSearch
Embeddings

Cloud and DevOps

AWS
Docker
GitHub Actions
Terraform

Observability

CloudWatch
OpenTelemetry
AI evaluation and cost monitoring

Capstone: customer-to-production AI solution

Participants work in teams on an enterprise use case and follow the complete FDE lifecycle.

THE FDE LIFECYCLE

Discover
Scope
Design

Build

Evaluate

Deploy

Demo

  • Example use cases
  • AI IT service desk
  • Enterprise knowledge assistant
  • Customer support agent
  • Research assistant
  • Claims processing assistant
  • Final output
  • Working prototype
  • Technical scope
  • Trade-off assessment
  • Evaluation results
  • Deployment plan
  • Executive customer demo

Outcomes: FDE ready

  • Frame and scope enterprise AI use cases with clear business outcomes.
  • Integrate AI solutions with enterprise data, APIs and business systems.
  • Deploy production-style AI applications using cloud and DevOps practices.
  • Demonstrate an end-to-end customer-to-production AI solution.
  • Rapidly build working LLM, RAG and agentic AI prototypes.
  • Evaluate AI quality, cost, latency, safety and production readiness.
  • Communicate technical options and trade-offs clearly to business and technology stakeholders.

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