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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer
- Distinguishing the role from ML engineer and solutions engineer
- Assuming responsibility for outcomes rather than just delivery milestones
- Collaborating with product, research, and security teams
Discovery in Complex Enterprises
- Observing a workflow from start to finish
- Mapping systems, data, and handovers
- Identifying the constraint that impedes value creation
Problem Scoping and Prioritisation
- Validating use cases for feasibility
- Scoring candidate workflows based on effort
- Selecting the initial workflow to prove
Value Framing and Acceptance Criteria
- Establishing baseline metrics before any development
- Using business case language for finance sponsors
- Agreeing acceptance criteria with end users
Model Selection: Frontier and Open Weights
- Comparing GPT-6, Claude 5.5, and open models
- Trade-offs regarding context, latency, and unit cost
- Situations where data rules necessitate locally hosted models
Context Engineering and Prompt Design
- Structuring instructions for consistent behaviour
- Assembling context from systems of record
- Implementing prompt caching and token minimisation
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content
- Hybrid search, reranking, and citation
- Evaluating retrieval independently from generation
Agents and Tool Use
- Tool design and typed interfaces
- Building with the OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure handling
MCP and Enterprise Integration
- The Model Context Protocol 2026-07-28 revision
- Developing and securing MCP servers
- Authorising tools against enterprise identity
Evaluation as the Delivery Language
- Creating golden sets from actual work
- Automated scoring and LLM judges
- Establishing release gates linked to evaluation thresholds
Guardrails, Safety, and Human Oversight
- Implementing guardrails at prompt, tool, and policy layers
- Defining escalation and human review points
- Maintaining audit trails and data handling duties
Deployment Architecture within the Customer Environment
- Constraints related to identity, network, and data residency
- Model gateway and multi-provider abstraction
- Infrastructure as code and release process
Observability, Cost, and Token Economics
- Tracing agent executions and tool calls
- Dashboards for quality and expenditure
- Reducing costs without compromising quality
Adoption and Change
- Empowering the people performing the work
- Champion networks and feedback loops
- Measuring adoption velocity
From Engagement to Reusable Capability
- Packaging proven workflows
- Playbooks, blueprints, and accelerators
- Transitioning to internal teams
Requirements
- Proficiency in Python, including working with APIs and virtual environments
- Familiarity with cloud platforms, containers, and version control
- Previous 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 transitioning to customer-facing AI delivery
- Technical consultants and solutions architects responsible for AI rollouts
- Platform engineers supporting production AI workloads
