GenAI Solutions & Delivery
Scope GenAI solutions with estimation, risk, and success criteria. Orchestrate delivery teams, manage client relationships.
What a GenAI solutions and delivery lead does · GenAI Solutions & Delivery
A GenAI project has a price, a date, a list of things that can go wrong and a definition of done, and the delivery lead is the person accountable for all four. You scope with estimation, risk and success criteria, orchestrate a delivery team, keep the client relationship honest through the inevitable surprises, and advise on a roadmap that outlasts the engagement. The technical floor is real: you design the architecture, build agent-based solutions with LangChain and LangGraph, customize enterprise LLM deployments, manage FinOps with Langfuse, Prometheus and Grafana, and package the result as deployable artifacts. The forward deployed engineer is your hands on the customer's infrastructure; the solutions architect sets the standards you deliver against.
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10 courses, each building on the last. The bar shows each course's share of the curriculum. 7 lessons are free to read.
Python Essentials for Agent Builders 5.4% of the curriculum
In scope for GenAI Solutions & Delivery: chapters 1–13 — 62 of this path’s 1141 lessons.
LLM Foundations for Agent Builders 5.7% of the curriculum
In scope for GenAI Solutions & Delivery: chapters 1–6, 14–20 — 65 of this path’s 1141 lessons.
Kubernetes Essentials for GenAI Engineers 5.8% of the curriculum
In scope for GenAI Solutions & Delivery: chapters 1–6, 8–12 — 66 of this path’s 1141 lessons.
Web APIs & Services for GenAI Engineers 5.3% of the curriculum
In scope for GenAI Solutions & Delivery: chapters 1–10 — 60 of this path’s 1141 lessons.
GenAI Agent Engineering 27.3% of the curriculum
In scope for GenAI Solutions & Delivery: chapters 11–59, 61, 70 — 312 of this path’s 1141 lessons.
Enterprise LLM Customization 21.6% of the curriculum
- Model Selection Engine
- Reasoning Model Benchmark
- TTC Controller
- Best-of-N Inference Engine
- Self-Refining Agent
- Compute-Optimal Router
- 6 of 41 chapters shownand 35 more
GenAI Operations 8.9% of the curriculum
In scope for GenAI Solutions & Delivery: chapters 2–3, 5, 26–28 and 11 more — 102 of this path’s 1141 lessons.
GenAI Architecture & Design Patterns 13.7% of the curriculum
- GenAI ADR EngineCreate ADR governance dashboard and compliance audit
- Streaming AI Pipeline
- Cell-Based AI Platform
- AI Observability Stack
- Layered AI Security System
- MCP Tool Mesh
- A2A Agent Network
- 7 of 26 chapters shownand 19 more
GenAI Evaluation, Safety & Governance 1.1% of the curriculum
In scope for GenAI Solutions & Delivery: chapters 20–21 — 12 of this path’s 1141 lessons.
AI Solution Delivery 5.3% of the curriculum
- AI Use Case Discovery & Data Readiness AssessmentGenerate executive discovery reports from structured assessment data
- Stakeholder Communication & Demo Engineering
- Delivery Risk Management & Governance
- POC to Production Hardening
- Knowledge Transfer & Training Automation
- Post-Delivery Support & SLA Monitoring
- Delivery Capstone — End-to-End AI Engagement
- 7 of 12 chapters shownand 5 more
Core responsibilities this discipline prepares you for. Open one to see how you build it.
Lead end-to-end GenAI project delivery from discovery through production handoff
- Run the complete delivery lifecycle: discovery workshops → problem scoping → rapid prototyping → handoff
- Drive evaluation-driven iteration with measurable quality gates and knowledge transfer
- Walk through each delivery phase with realistic client scenarios including scoping and risk assessment
Design GenAI architecture for client engagements
- Apply cell-based AI, MCP mesh, and multi-tenant architecture patterns to client requirements
- Write ADR documentation with reference designs and technology evaluation rationale
- Create architecture proposals for varied client scenarios and defend design decisions under review
Build agent-based solutions for client business processes
- Design LangGraph agents with MCP tool integration and human-in-the-loop approval gates
- Customize agent behavior, tool access, and workflow logic for different business process requirements
- Build and deploy domain-specific agents adapted to varied client business scenarios
Customize enterprise LLM deployments — gateways, RAG, domain adaptation
- Operate LiteLLM gateways with multi-provider management and enterprise RAG stack customization
- Adapt LLM deployments for healthcare, finance, and legal verticals with domain-specific constraints
- Deliver end-to-end LLM customization for regulated industries with compliance validation
Manage FinOps for client GenAI projects
- Build token cost attribution models with budget forecasting and TCO analysis for proposals
- Design cost optimization strategies across providers, caching tiers, and model selection
- Build cost models, forecast annual spend, and present optimization recommendations to stakeholders
Scope project timelines and team requirements
- Apply effort estimation frameworks designed for non-deterministic GenAI project delivery
- Map team skills, assess technical risks, and develop detailed project proposals
- Estimate effort for sample GenAI projects and identify optimal team composition and skill coverage
Package solutions as deployable artifacts for client operations teams
- Build Helm charts with operational runbooks, SLA definitions, and integrated monitoring
- Create client handoff documentation with deployment guides and escalation procedures
- Package a complete GenAI solution and conduct a simulated client handoff with operational validation
Advise clients on technology roadmaps with emerging GenAI patterns
- Evaluate emerging patterns: A2A protocol, MCP mesh, cell-based AI, and multi-tenant architectures
- Assess industry trends, adoption timelines, and migration strategies for client technology stacks
- Build technology roadmap recommendations that balance innovation with operational stability
Each group becomes a signed node on your public skill graph — a recruiter-auditable record of the labs you actually passed. Share the URL; they see the discipline, the labs, and the signature.
Taking a delivered GenAI POC to production: transition playbooks, observability/SLA instrumentation, robustness hardening, capacity planning. The lab-provable delivery deliverable.
Eval-driven delivery quality: CI/CD quality gates, golden-trajectory regression suites, RAGAS/DeepEval pipelines, LLM-judge calibration. The strongest lab-assessable bar for this role.
Ensuring a delivery conforms to the target architecture: ADR/reference-architecture conformance, technical-standards adherence, architectural-quality gates. The lab-checkable conformance face (the review-judgment half is reserved for the portfolio track).
Hands-on technical depth: agent loops, ReAct, MCP, multi-agent orchestration, production RAG, vector + graph DB engineering. Demonstrable in labs.
Running the delivered solution: incident-response runbooks, automation, cost attribution/chargeback, token-budget controllers, capacity forecasting, FinOps.
The technical/automatable face of delivery governance: compliance pre-flight (EU AI Act/SOC2/HIPAA), regulatory-artifact generation, change-control gates, risk-register tooling. Governance-committee facilitation is reserved for the portfolio track.
The lab-assessable feasibility face of scoping: data-readiness assessment, technical feasibility analysis, effort estimation from technical signals, success-criteria definition. Live client discovery/qualification is reserved for the portfolio track.
The buildable half of delivery framing: rapid prototyping for client validation, scaffold libraries, demo engineering, synthetic-data scaffolds. Delivery-framework design is reserved for the portfolio track.
Provider SDK literacy: OpenAI/Anthropic/Gemini integration, multi-provider abstraction. Prerequisite.
Production-grade Python for delivery tooling: async, Pydantic, typing, pytest. Prerequisite.