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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer
- The role versus ML engineer and solutions engineer
- Owning outcomes rather than delivery milestones
- Working with product, research and security teams
Discovery in Messy Enterprises
- Shadowing a workflow end to end
- Mapping systems, data and handoffs
- Finding the constraint that blocks value
Problem Scoping and Prioritisation
- Qualifying use cases for feasibility
- Scoring candidate workflows against effort
- Choosing the first workflow to prove
Value Framing and Acceptance Criteria
- Baselining metrics before any build
- Business case language for finance sponsors
- Agreeing acceptance criteria with users
Model Selection: Frontier and Open Weights
- Comparing GPT-6, Claude 5.5 and open models
- Context, latency and unit cost trade-offs
- When data rules force locally hosted models
Context Engineering and Prompt Design
- Structuring instructions for reliable behaviour
- Assembling context from systems of record
- Prompt caching and token minimisation
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content
- Hybrid search, reranking and citation
- Evaluating retrieval separately from generation
Agents and Tool Use
- Tool design and typed interfaces
- Building with the OpenAI and Claude agent SDKs
- Approvals, retries and failure handling
MCP and Enterprise Integration
- The Model Context Protocol 2026-07-28 revision
- Building and securing MCP servers
- Permissioning tools against enterprise identity
Evaluation as the Delivery Language
- Golden sets built from real work
- Automated scoring and LLM judges
- Release gates tied to eval thresholds
Guardrails, Safety and Human Oversight
- Guardrails at prompt, tool and policy layers
- Escalation and human review points
- Audit trails and data handling duties
Deployment Architecture in the Customer Estate
- Identity, network and data residency constraints
- Model gateway and multi-provider abstraction
- Infrastructure as code and release process
Observability, Cost and Token Economics
- Tracing agent runs and tool calls
- Dashboards for quality and spend
- Cutting cost without losing quality
Adoption and Change
- Enablement for the people doing the work
- Champion networks and feedback loops
- Measuring adoption velocity
From Engagement to Reusable Capability
- Packaging proven workflows
- Playbooks, blueprints and accelerators
- Handing over to internal teams
Requirements
- Proficiency in Python, including working with APIs and virtual environments
- Familiarity with cloud platforms, containers and version control
- Prior experience calling an LLM API or building a prompt-based feature
- Basic understanding of enterprise systems such as CRM, ERP or ticketing platforms
Audience
- Forward deployed and applied AI engineers
- Software engineers moving into customer-facing AI delivery
- Technical consultants and solutions architects responsible for AI rollouts
- Platform engineers supporting production AI workloads
