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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer
- Distinguishing the role from ML engineers and solutions engineers
- Focusing on business outcomes rather than delivery milestones
- Collaborating effectively with product, research, and security teams
Discovery in Complex Enterprise Environments
- Mapping workflows end-to-end through shadowing
- Charting systems, data flows, and handoff points
- Identifying the primary constraints hindering value creation
Problem Scoping and Prioritisation
- Assessing use cases for technical and operational feasibility
- Ranking candidate workflows based on effort required
- Selecting the initial workflow for proof-of-concept
Value Framing and Acceptance Criteria
- Establishing baseline metrics prior to development
- Articulating the business case for financial stakeholders
- Defining acceptance criteria in collaboration with end-users
Model Selection: Frontier and Open-Weight Options
- Comparing GPT-6, Claude 5.5, and open-source models
- Balancing trade-offs in context window, latency, and unit cost
- Determining when data regulations necessitate locally hosted models
Context Engineering and Prompt Design
- Structuring instructions to ensure consistent model behaviour
- Aggregating context from authoritative systems of record
- Optimising costs through prompt caching and token reduction
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content effectively
- Implementing hybrid search, reranking, and citation mechanisms
- Evaluating retrieval accuracy independently from generation quality
Agents and Tool Integration
- Designing tools and typed interfaces for agent interaction
- Building solutions using OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure recovery strategies
MCP and Enterprise Integration
- Implementing the Model Context Protocol 2026-07-28 revision
- Developing and securing MCP servers
- Aligning tool permissions with enterprise identity management
Evaluation as the Core of Delivery
- Constructing golden sets derived from real-world work
- Utilising automated scoring and LLM-based judges
- Establishing release gates linked to evaluation thresholds
Guardrails, Safety, and Human Oversight
- Implementing guardrails at prompt, tool, and policy levels
- Defining escalation paths and human review checkpoints
- Maintaining audit trails and fulfilling data handling obligations
Deployment Architecture within Client Infrastructure
- Addressing identity, network, and data residency requirements
- Implementing model gateways and multi-provider abstraction
- Utilising Infrastructure as Code and standardised release processes
Observability, Cost, and Token Economics
- Tracing agent executions and tool invocations
- Creating dashboards for quality monitoring and spend tracking
- Reducing operational costs while maintaining quality standards
Adoption and Change Management
- Enabling the personnel who execute the work
- Building champion networks and feedback loops
- Tracking the velocity of user adoption
Transitioning from Engagement to Reusable Capability
- Packaging proven workflows for future use
- Developing playbooks, blueprints, and accelerators
- Facilitating handover to internal teams
Requirements
- Proficiency in Python, including API integration and virtual environment management
- Familiarity with cloud platforms, containerisation, and version control systems
- Previous experience interacting with LLM APIs or developing prompt-based features
- Foundational knowledge of enterprise systems such as CRM, ERP, or ticketing platforms
Target Audience
- Forward deployed and applied AI engineers
- Software engineers transitioning into client-facing AI delivery roles
- Technical consultants and solutions architects leading AI implementation
- Platform engineers supporting production AI workloads
