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
11 skill groups · each becomes a signed node on your graph.
Verifiable skill graph
11 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.
Rapid prototyping of AI solutions: turning an ambiguous customer problem into a demoable end-to-end slice under a time box, with deliberate throwaway-vs-keep tradeoffs. The role's headline deliverable.
Integrating GenAI with the customer's existing data and workflows under messy real-world constraints: schemas you didn't design, access/PII controls you must honor, immutable upstream systems, data-residency limits. Distinct from generic data engineering.
Customer discovery and solution scoping: requirement elicitation, success-criteria definition, feasibility assessment, and framing the AI engagement before building.
Deploying AI workloads inside the customer's security boundary: their VPC/IAM, customer-managed secrets/KMS, their identity provider, network policy, egress and data-residency constraints, air-gapped environments. The hardest-to-fake FDE differentiator.
Taking a working proof-of-concept to production in a customer engagement: reliability, error handling, monitoring, productionization tradeoffs, runbooks and handoff quality.
Managing the delivery of an AI engagement: milestones, scope and risk burn-down, stakeholder expectation-setting, delivery risk under customer constraints, handoff quality. The "scope and manage delivery" responsibility, at IC level.
The integrative end-to-end engagement: demo delivery, stakeholder handoff and readout, engagement summary, and the field-learnings / product-feedback writeup. Absorbs the (unmeasurable-in-isolation) stakeholder-communication dimension as authentic capstone artifacts.
RAG pipelines, retrieval, and fine-tuning/customization depth — the technical substrate a forward-deployed engineer exercises while prototyping for customers.
Agent engineering depth: tool use, orchestration, multi-agent patterns — the technical substrate a forward-deployed engineer exercises while prototyping for customers.
Provider SDK integration: client setup, multi-provider calls, streaming, error handling. Prerequisite plumbing.
Production-grade Python for forward-deployed work: async, typing, packaging, scripting against customer systems. Prerequisite.
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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