GenAI Engineering Director

EM / Director ยท 8 courses ยท 164 chapters

Hire and build GenAI engineering teams, design team structures for GenAI, set engineering quality frameworks.

What you'll learn

Core responsibilities this discipline prepares you for.

1

Hire and build GenAI engineering teams

  • Define GenAI-specific hiring criteria and design technical interviews for LLM and agent engineering roles
  • Build skill assessment frameworks and team composition strategies balancing generalist and specialist profiles
  • Write job descriptions, design interview rubrics, and evaluate candidates against GenAI competency matrices
2

Define engineering processes

for GenAI development โ€” eval-driven workflows

  • Design GenAI-specific sprint planning with eval-driven development as the core feedback loop
  • Define evaluation metrics before writing code and measure GenAI team velocity with non-deterministic outputs
  • Build team workflows integrating Langfuse for evaluation tracking and Grafana for velocity metrics
3

Manage quality and team performance

for GenAI outputs

  • Define GenAI quality metrics and SLA management frameworks for LLM system reliability
  • Build team performance dashboards using Grafana with latency, quality, and throughput indicators
  • Construct performance dashboards and define quality standards for GenAI engineering deliverables
4

Understand the technical stack

deeply enough to unblock teams

  • Learn LLM fundamentals, LangGraph agent engineering patterns, and LiteLLM gateway operations
  • Monitor production systems with Langfuse and Prometheus to review PRs and debug incidents
  • Gain sufficient depth to make architecture calls, review designs, and unblock teams on technical decisions
5

Operate and budget for GenAI infrastructure

โ€” FinOps and capacity

  • Build LLM cost attribution dashboards with capacity planning and budget forecasting models
  • Manage vendor relationships and optimize spend allocation across multiple LLM providers
  • Construct FinOps dashboards, set team-level token budgets, and produce monthly cost reports for leadership
6

Design organization structure

for GenAI engineering teams

  • Apply GenAI team topology patterns including on-call rotation design and knowledge sharing practices
  • Evaluate embed-vs-centralize tradeoffs for GenAI engineering functions across the organization
  • Design org structures for different company sizes with clear ownership boundaries and escalation paths
7

Drive technical strategy

โ€” evaluate new tools and plan migrations

  • Apply technology evaluation frameworks with structured criteria for GenAI tool and platform selection
  • Build migration planning methodology and strategic roadmaps for technology transitions
  • Evaluate new tools against defined criteria, build migration plans, and present strategy to leadership
8

Ensure responsible AI practices

across your team

  • Design governance policies and safety review processes for GenAI system development and deployment
  • Build compliance workflows and team-level responsible AI standards with enforcement mechanisms
  • Create governance policies and integrate safety review checkpoints into the development lifecycle

Your learning path

8 courses ยท sequenced for compounding ยท 164 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 โ€” 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 โ€” auth, streaming, rate limiting, OpenAPI contracts.

Advanced16 Ch

Step 5

GenAI Agent Engineering

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

Advanced11 Ch

Step 6

Enterprise LLM Customization

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

Advanced10 Ch

Step 7

GenAI Operations

Run GenAI in production โ€” monitoring, dunning, incident response, cost control, and the on-call runbook.

Advanced65 Ch

Capstone

GenAI Engineering Leadership

Lead GenAI engineering orgs โ€” hiring, roadmap, tech strategy, budgeting, and managing the unique risks of AI teams.

GenAI stack that you will run labs

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

Prometheus

Team-level metrics for planning labs

Grafana

Cost + velocity dashboards in labs

Langfuse

Quality observability for EM labs

LangChain

Framework your ICs build on

OpenAI API

Budget-allocation labs

Anthropic API

Model procurement labs

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