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Forward Deployed GenAI Engineering

L5-L6 · 8 courses · 141 chapters

Rapid-prototype GenAI solutions on customer infrastructure, integrate GenAI with customer data and workflows, scope solutions with delivery methodology.

What you'll learn

Core responsibilities this discipline prepares you for.

Embed on-site with clients

to discover GenAI opportunities and scope projects

Run structured discovery sessions: stakeholder interviews, process mapping, and opportunity scoring
Score automation opportunities by ROI and write scope documents with acceptance criteria
Simulate realistic client discovery engagements with estimation and risk assessment exercises

Build rapid prototypes

that demonstrate GenAI value within weeks

Go from problem statement to working prototype using LangGraph agent logic and MCP tool integration
Iterate prototypes based on evaluation metrics and present results to stakeholders
Build end-to-end prototypes under time constraints with evaluation-driven iteration cycles

Integrate GenAI into client data systems

— databases, APIs, and legacy systems

Connect LLM applications to PostgreSQL, pgvector, Redis, Kafka, MinIO, and REST APIs
Build data ingestion pipelines that feed RAG systems from existing databases and legacy endpoints
Implement common enterprise integration patterns with real database connections and API adapters

Customize LLM applications

for client-specific domains (healthcare, finance, legal)

Build domain-specific RAG pipelines with HIPAA-compliant PII detection using Presidio
Construct financial RAG systems with regulatory citation and legal contract analysis pipelines
Validate domain-specific compliance constraints across regulated industry scenarios

Deploy solutions as packaged Helm charts

clients can operate independently

Package GenAI applications as self-contained Helm charts with Kustomize overlays per environment
Write operational runbooks and define SLAs with integrated monitoring and alerting
Simulate a complete solution handoff including packaging, documentation, and operational validation

Build GenAI agent workflows

tailored to client business processes

Design LangGraph agents with human-in-the-loop approval gates and MCP-based tool integration
Customize agent behavior for different business process requirements and approval hierarchies
Build and deploy domain-specific agents adapted to varied client business scenarios

Manage LLM provider costs

and build FinOps models for client engagements

Optimize multi-provider costs via LiteLLM routing with cost-per-request modeling
Build ROI estimation frameworks and pricing models for client proposals
Tune provider selection strategies across usage scenarios to hit target cost margins

Configure enterprise guardrails

to meet client compliance requirements

Set up NeMo Guardrails for content safety and Presidio for multi-language PII detection
Configure compliance-specific policies aligned with SOC2, HIPAA, and GDPR requirements
Validate guardrail configurations against adversarial test suites in regulated industry scenarios

Your learning path

8 courses · sequenced for compounding · 141 chapters

Beginner13 Ch

Foundations

Python Essentials for Agent Builders

Master Python fundamentals from zero to professional code structure. Builds incrementally toward agent-ready patterns.

Intermediate20 Ch

Step 2

LLM Foundations for Agent Builders

Deep understanding of LLM internals, data pipelines, architecture, and multi-provider integration patterns.

Intermediate17 Ch

Step 3

Kubernetes Essentials for GenAI Engineers

Ship GenAI workloads on K8s — pods, services, Helm, GPU scheduling, and production-grade deployment patterns.

Intermediate12 Ch

Step 4

Web APIs & Services for GenAI Engineers

Design, build, and harden HTTP APIs with FastAPI — auth, streaming, rate limiting, OpenAPI contracts.

Intermediate10 Ch

Step 5

Data Infrastructure Essentials for GenAI

Kafka, pgvector, object stores, and data pipelines — the storage spine under every production GenAI system.

Advanced16 Ch

Step 6

GenAI Agent Engineering

Build production-grade agents with hosted LLMs — planning, tools, memory, evaluation, and orchestration patterns.

Advanced11 Ch

Step 7

Enterprise LLM Customization

Customize LLMs for enterprise — prompt engineering, RAG at scale, fine-tuning, and domain adaptation techniques.

Advanced42 Ch

Capstone

AI Solution Delivery

Deliver GenAI solutions for customers — discovery, scoping, architecture review, rollout, and handover.

GenAI stack that you will run labs

Tools and APIs you invoke directly from every lab in this discipline — not the infrastructure GenBodha uses to host them.

LangChain

Build production agent chains for customers

LangGraph

Orchestrate long-running customer workflows

MCP SDK

Plug customer systems into agents via MCP

OpenAI API

GPT-4o powered customer-facing agents

Anthropic API

Claude for document-heavy enterprise labs

pgvector

Per-tenant vector search for customer RAG

PostgreSQL

Multi-tenant state store

Redis

Sessions + rate limits per tenant

MinIO

S3-compatible store for customer files

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