Turn your engineers into the people who ship AI into production — not just prototype it.
A 25-day, 200-hour immersive programme built on real enterprise deployments.
25-Day Curriculum
Security by Design: security is built in at every layer — on Days 7, 13 and 16 — not bolted on after the build.
What is a Forward-Deployed Engineer?
FDE vs Software Engineer vs Consultant vs Solutions Architect
FDE mindset
Working with ambiguous requirements
Enterprise GenAI landscape
AI opportunity identification
Business value vs technology-first thinking
Introduction to the BRIDGE Method™
Outcome: Understand the FDE role and the complete business-to-deployment lifecycle.
Stakeholder discovery & interview techniques
Business process mapping
Current-state analysis
Systems and data discovery
Workflow documentation
Pain-point identification
Baseline metrics
Scoping an AI engagement
Artifact: Scoping Sheet
Enterprise data landscape: structured vs unstructured data, operational databases, warehouses, lakes/lakehouses, APIs, documents and knowledge repositories
Data for GenAI: ingestion, preparation, metadata, quality, freshness, ownership, classification
AI data architecture: pipelines, ETL/ELT, vector databases, knowledge graphs, metadata stores
Data governance: lineage, cataloguing, access, retention, PII, sensitive data and residency
Exercise: assess a messy environment of databases, SharePoint, PDFs, CRM and APIs for relevance, ownership, trustworthiness, access, LLM exclusions and preprocessing needs
Artifact: Enterprise AI Data Assessment (extends the Scoping Sheet)
Problem framing
Root-cause analysis
Workflow decomposition
LLM vs deterministic tasks
Human-in-the-loop
AI opportunity identification
Use-case prioritization
AI solution patterns
Initial solution architecture
Artifact: Reframe Canvas
Security-first mindset for AI
Identity & access basics: authentication, authorization, RBAC, IAM and service identities
Data security: PII, sensitive data, encryption, secrets management and data leakage
LLM threat landscape: prompt injection, jailbreaking, data exfiltration, malicious documents, context manipulation and model abuse
Artifact: AI Security Threat Model — starts here
LLM fundamentals
Model selection
Prompt engineering
Context management
Structured outputs
Function calling
Tool use
Embeddings
Vector databases
API-based LLM applications
RAG architecture
Document ingestion
Chunking strategies
Embeddings
Retrieval
Reranking
Metadata filtering
Hybrid search
Citations
RAG evaluation
Enterprise knowledge systems
Document-level permissions
Access-controlled retrieval
Data poisoning
Tenant isolation
Applying data governance — lineage, classification and residency — to the RAG build
Artifact: AI Security Threat Model — extends the initial threat model
AI agents
Tool use
Agent architecture
MCP
MCP servers
LangGraph
Agent orchestration
State management
Multi-agent systems
Human-in-the-loop workflows
Agent failure modes
Tool permissions
Agent identity
Excessive agency
MCP security
Tool poisoning
Privilege escalation
Human approval workflows
Hands-on: harden a deliberately insecure AI agent with prompt injection, excessive tool permissions, sensitive data exposure, insecure retrieval and missing authentication
Artifact: AI Security Threat Model — completes the threat model
Why AI evaluation matters
Evaluation-driven development
Evaluation & golden datasets
Accuracy, relevance and faithfulness
Hallucination
Retrieval metrics
LLM-as-a-judge
Regression testing
Evaluation pipelines
Measuring latency and cost
Participants evaluate their own capstone solution
Artifact: Eval Charter
MLOps fundamentals: ML lifecycle, experiment tracking, model/dataset/version management, CI/CD and deployment strategies
Production AI lifecycle: Data → Model → Prompt → Application → Evaluation → Deployment → Monitoring → Feedback
Hands-on: build a Dev → Test → Evaluation → Production pipeline
Artifact: AI/LLMOps Deployment Pipeline
Callout: A successful prototype is not a production system.
Production architecture, API design and containerisation
Guardrails, observability, logging and monitoring
Reliability: failure handling, fallback models, human escalation, rate limits, retries, disaster recovery and business continuity
Cost engineering: token economics, model selection, caching, routing, batch processing and FinOps for AI
Scaling
Deployment on AWS Bedrock, Azure AI Foundry and Google Cloud enterprise AI/agent platform
Model access, enterprise integrations and production readiness
Enterprise AI architecture: data, model, RAG, agent, application, security, observability and governance layers
Responsible AI and AI governance
Model risk and AI policies
Human oversight, auditability and compliance
Data & model governance
End-to-end observability
Artifact: Enterprise AI Architecture & Governance Blueprint
Technical & architecture documentation
Runbooks
Knowledge transfer
Client-team enablement
Training the customer
Change management
Operational readiness
Measuring adoption
ROI measurement
Artifact: Handoff Runbook
Business problem
Baseline
Reframe
Evaluation strategy
Solution architecture
Working AI system
Evaluation results
Security & governance
Deployment
Handoff
ROI / business impact
Capstone flow: Business Problem → Baseline → Reframe → Evaluation Strategy → Solution Architecture → Working AI System → Evaluation Results → Security & Governance → Deployment → Handoff → ROI / Business Impact
Final presentation: Technical Demonstration and Executive Business Case.
What You'll Be Able To Do
Discover and diagnose enterprise AI opportunities.
Translate ambiguous business requirements into implementable AI solutions.
Decompose workflows into LLM, agentic and deterministic components.
Build production-grade RAG applications.
Develop AI agents and multi-agent workflows.
Build and integrate MCP servers and tools.
Use LangGraph for agent orchestration.
Design evaluation frameworks before deploying AI.
Measure accuracy, reliability, latency and cost.
Implement guardrails, security and observability.
Deploy enterprise AI on AWS, Azure or Google Cloud.
Create technical documentation and operational runbooks.
Conduct client handover and enablement.
Quantify business impact and ROI.
Present AI solutions to technical and executive stakeholders.
Live Capstone: One Problem, All 25 Days
Day 1: Receive the problem
Receive a real enterprise business problem.
Days 3–5: Baseline the organisation and its data
Map workflows, systems, data, stakeholders, access and current-state measures.
Day 6: Reframe the problem
Prioritise the AI opportunity and design the solution approach.
Days 7–16: Build securely
Develop the RAG, agentic or deterministic system while applying security by design.
Days 17–18: Create and run evaluations
Define the evaluation plan, test the system and address quality gaps.
Days 19–23: Operationalise and deploy
Build the LLMOps pipeline, harden the system, deploy it and establish governance.
Day 24: Prepare handoff
Create the runbook, documentation, enablement plan and ROI case for the client team.
Day 25: Client simulation + executive business case
Present the technical demonstration, evaluation evidence and business impact.
Programme Artifacts
Scoping Sheet
Frames the business problem, stakeholders, systems, data and baseline metrics.
BASELINE
Enterprise AI Data Assessment
Assesses data relevance, ownership, trust, access and preparation needs for AI.
BASELINE
Reframe Canvas
Turns an ambiguous business need into a prioritised AI solution design.
REFRAME
AI Security Threat Model
Maps AI-specific security, identity, data and tool-use risks across the build.
GOVERN
Eval Charter
Defines success metrics, golden datasets and the evaluation plan.
INSTRUMENT
AI/LLMOps Deployment Pipeline
Documents the Dev-to-Production path for prompts, models, evaluations and monitoring.
GOVERN
Enterprise AI Architecture & Governance Blueprint
Connects architecture, governance, observability and operating controls.
GOVERN
Handoff Runbook
Guides operational handoff, knowledge transfer, adoption and ROI measurement.
ENABLE
Forward Deployed Engineer Academy FAQs
Who is the Forward Deployed Engineer Academy for?
It is designed for software engineers, data engineers, solution architects and technical leads who need to take enterprise AI use cases from discovery through deployment.
How long is the programme?
The programme runs for 25 days and 200 hours, combining instructor-led learning, hands-on labs and an end-to-end capstone.
What are the prerequisites?
Participants should be comfortable with software development fundamentals and working with APIs. Familiarity with Python, cloud platforms or LLM concepts is helpful but not required.
What delivery modes are available?
The programme is delivered live either on-site at your location or online through virtual instructor-led training (VILT).
What will participants be able to do after the programme?
Participants will be able to discover AI opportunities, build RAG and agentic systems, design evaluations, implement security and observability, deploy on enterprise cloud platforms, and hand over a documented solution.
Is a certificate provided?
Yes. Participants who complete the programme receive a Stalwart Learning Forward Deployed Engineer Certificate.