GenAI Engineering Director

EM / Director · 492h · 8 courses · 164 chapters

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

Role-alignedHands-on labsCapstone project30-day money-back

What you'll own in this role

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 — 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

— 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

— 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

Tools you'll ship with

Industry-standard stack for current L4–L6 GenAI engineering roles.

PrometheusGrafanaLangfuseK8sLangChainOpenAI APIAnthropic API

Your learning route

8 courses · sequenced for compounding · 164 chapters · ~492 hours

Step 1 · Foundations

Python Essentials for Agent Builders

13 chapters

Step 2

LLM Foundations for Agent Builders

20 chapters

Step 3

Kubernetes Essentials for GenAI Engineers

17 chapters

Step 4

Web APIs & Services for GenAI Engineers

12 chapters

Step 5

GenAI Agent Engineering

16 chapters

Step 6

Enterprise LLM Customization

11 chapters

Step 7

GenAI Operations

10 chapters

Step 8 · Capstone

GenAI Engineering Leadership

65 chapters

Start the GenAI Engineering Director discipline today

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