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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer
- Distinguishing the role from that of an ML engineer or solutions engineer
- Focusing on outcomes rather than mere delivery milestones
- Collaborating with product, research, and security teams
Discovery in Complex Enterprises
- Observing workflows end-to-end
- Mapping systems, data flows, and handoff points
- Identifying the core constraints limiting value
Problem Scoping and Prioritisation
- Qualifying use cases based on feasibility
- Scoring candidate workflows relative to effort
- Selecting the initial workflow for validation
Value Framing and Acceptance Criteria
- Establishing baseline metrics prior to development
- Using business language for finance stakeholders
- Agreeing on acceptance criteria with end users
Model Selection: Frontier and Open Weights
- Comparing GPT-6, Claude 5.5, and open-source models
- Balancing context, latency, and unit cost
- Determining when data regulations require locally hosted models
Context Engineering and Prompt Design
- Structuring instructions for consistent behaviour
- Gathering context from systems of record
- Implementing prompt caching and minimising token usage
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content
- Utilising hybrid search, reranking, and citation
- Evaluating retrieval quality separately from generation
Agents and Tool Use
- Designing tools and typed interfaces
- Building with OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure states
MCP and Enterprise Integration
- The Model Context Protocol 2026-07-28 revision
- Building and securing MCP servers
- Aligning tool permissions with enterprise identity
Evaluation as the Delivery Language
- Creating golden sets from real work
- Applying automated scoring and LLM judges
- Setting release gates based on evaluation thresholds
Guardrails, Safety and Human Oversight
- Implementing guardrails at prompt, tool, and policy levels
- Defining escalation and human review points
- Maintaining audit trails and data handling responsibilities
Deployment Architecture in the Customer Estate
- Addressing identity, network, and data residency constraints
- Using model gateways and multi-provider abstractions
- Applying infrastructure as code and release processes
Observability, Cost and Token Economics
- Tracing agent runs and tool interactions
- Creating dashboards for quality and expenditure
- Reducing costs without compromising quality
Adoption and Change
- Enabling the workforce performing the work
- Building champion networks and feedback loops
- Measuring adoption velocity
From Engagement to Reusable Capability
- Packaging validated workflows
- Developing playbooks, blueprints, and accelerators
- Transferring ownership to internal teams
Requirements
- Proficiency in Python, including experience with APIs and virtual environments
- Familiarity with cloud platforms, containerisation, and version control
- Previous experience interacting with LLM APIs or developing prompt-based features
- Basic understanding of enterprise systems such as CRM, ERP, or ticketing platforms
Target Audience
- Forward deployed and applied AI engineers
- Software engineers transitioning to client-facing AI delivery
- Technical consultants and solutions architects overseeing AI rollouts
- Platform engineers supporting production AI workloads
