Forward Deployed GenAI Engineering
Rapid-prototype GenAI solutions on customer infrastructure, integrate GenAI with customer data and workflows, scope solutions with delivery methodology.
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 for customer-on-site delivery: async/await, Pydantic for customer-data models, typing, dataclasses, pytest for delivery harnesses, error handling.
Provider SDK calls in customer prototypes: OpenAI/Anthropic/Gemini integration, LiteLLM multi-provider abstraction for customer flexibility, provider routing.
AI use-case discovery, data-readiness assessments, solution scoping, effort estimation, opportunity qualification, fit/feasibility analysis.
Rapid prototype harnesses, Streamlit/Gradio demo apps, synthetic data factories for prototypes, scaffold generation, iterative scope shaping with customers.
Customer data ingestion pipelines, PII handling in customer data, secure data exchange, schema-on-read patterns, customer-data sandboxing.
Deploying in customer security boundaries, GKE / on-prem K8s adaptation, customer-VPC + private networking, secrets management in customer envs, Terraform/Pulumi.
Demo engineering, executive briefings, customer training automation, knowledge-transfer workflows, presentation generation, customer onboarding.
POC-to-production transition playbooks, observability instrumentation, SLA monitoring, post-delivery support, robustness hardening, capacity planning.
Delivery risk identification, governance frameworks (SOW + change orders), regulatory + safety pre-checks (EU AI Act, SOC2/HIPAA mappings), customer compliance audits.
Agent loops, ReAct/planner-executor patterns, tool use + MCP, state graphs, multi-agent orchestration — full depth for customer-facing agent prototypes.
Production RAG pipelines, vector DB engineering, fine-tuning for customer-domain models, RLVR + reward engineering, GraphRAG, agentic orchestration for customer use cases.
End-to-end AI engagements (discovery → scoping → prototype → deploy → handoff), capstone projects synthesizing all forward-deployed skills, field-learning feedback loops.
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
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
- 2
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
- 3
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
- 4
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
- 5
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
- 6
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
- 7
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
- 8
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
Curriculum
9 courses · each builds on previous goals
Curriculum
9 courses · each builds on previous goals
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