GenAI Solutions & Delivery
Scope GenAI solutions with estimation, risk, and success criteria. Orchestrate delivery teams, manage client relationships.
Verifiable skill graph
12 skill groups · each becomes a signed node on your graph.
Verifiable skill graph
12 skill groups · each becomes a signed node on your graph.
Every lab you pass signs a W3C Verifiable Credential on your public skill graph. Completing the labs in each group below mints one node on that graph — the badge you walk away with is a cryptographic record of what you can ship, not a completion certificate.
Share the URL on your résumé or with a hiring manager. They click; they see the discipline, the labs you passed, and the verification signature. No honor system, no broker.
Production-grade Python applied to delivery oversight tooling: async/await, Pydantic models for delivery artifacts, typing, dataclasses, pytest for delivery-quality harnesses.
Provider SDK literacy needed to oversee solution prototypes: OpenAI/Anthropic/Gemini integration patterns, multi-provider abstraction for solution flexibility.
AI use-case discovery, opportunity qualification, solution scoping, effort estimation, data-readiness assessment, feasibility analysis, success-criteria definition.
Statement-of-work generation, proposal authoring, change-order workflows, deliverable definitions, acceptance criteria authoring, contractual milestone planning.
Delivery framework design, rapid prototyping for client validation, scaffold libraries, demo engineering, iterative scope shaping, synthetic data factories.
Executive briefings, client communication, knowledge-transfer automation, training delivery, demo engineering, status reporting, expectation management.
ADR alignment, reference architecture compliance, architecture review oversight, technical-standards adherence, architectural-quality gates.
Eval-driven CI/CD oversight, quality gates in delivery, golden-trajectory regression suites, RAGAS/DeepEval pipelines, LLM-judge calibration for QA.
Risk registers, governance committee workflows, compliance pre-flight (EU AI Act/SOC2/HIPAA), regulatory artifact generation, change-control + RACI matrices.
POC-to-production transition playbooks, observability instrumentation, SLA monitoring, post-delivery support, robustness hardening, capacity planning oversight.
Agent loops + ReAct + MCP + multi-agent orchestration, production RAG pipelines, vector + graph DB engineering — depth needed to oversee solution feasibility.
Incident response oversight, runbook automation, cost attribution + chargeback, token budget controllers, capacity forecasting, FinOps governance for delivered solutions.
What you'll ship in production
Core responsibilities this discipline prepares you for.
What you'll ship in production
Core responsibilities this discipline prepares you for.
- 1
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
- 2
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
- 3
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
- 4
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
- 5
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
- 6
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
- 7
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
- 8
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
Curriculum
10 courses · each builds on previous goals
Curriculum
10 courses · each builds on previous goals
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