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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 outcomes rather than mere delivery milestones
- Collaborating with product, research, and security teams
Discovery in Complex Enterprise Environments
- Tracking workflows from start to finish
- Mapping systems, data flows, and handover points
- Identifying constraints that hinder value creation
Problem Scoping and Prioritisation
- Validating use cases for technical feasibility
- Ranking candidate workflows based on effort required
- Selecting the initial workflow for proof-of-concept
Value Framing and Acceptance Criteria
- Establishing metric baselines prior to development
- Articulating the business case for finance stakeholders
- Aligning on acceptance criteria with end-users
Model Selection: Frontier and Open-Weight Options
- Evaluating GPT-6, Claude 5.5, and open-source models
- Weighing trade-offs between context, latency, and unit costs
- Determining when data regulations mandate local hosting
Context Engineering and Prompt Design
- Structuring instructions to ensure reliable behaviour
- Assembling context from core systems of record
- Implementing prompt caching and token efficiency strategies
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content effectively
- Utilising hybrid search, reranking, and citation techniques
- Assessing retrieval performance independently from generation
Agents and Tool Integration
- Designing tools and defining typed interfaces
- Developing with OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure recovery
MCP and Enterprise Integration
- Exploring the Model Context Protocol revision of July 28, 2026
- Constructing and securing MCP servers
- Aligning tool permissions with enterprise identity frameworks
Evaluation as the Core of Delivery
- Creating golden sets derived from real-world work
- Applying automated scoring and LLM-based judging
- Linking release gates to specific evaluation thresholds
Guardrails, Safety, and Human Oversight
- Implementing guardrails across prompt, tool, and policy layers
- Defining escalation paths and human review checkpoints
- Maintaining audit trails and fulfilling data handling obligations
Deployment Architecture within the Client Environment
- Navigating identity, network, and data residency requirements
- Implementing model gateways and multi-provider abstractions
- Utilising infrastructure as code and structured release processes
Observability, Cost, and Token Economics
- Tracing agent execution and tool interactions
- Maintaining dashboards for quality metrics and expenditure
- Reducing costs while preserving output quality
Adoption and Change Management
- Empowering the workforce executing the tasks
- Cultivating champion networks and feedback cycles
- Tracking the velocity of adoption
From Single Projects to Reusable Capabilities
- Encapsulating proven workflows
- Creating playbooks, blueprints, and accelerators
- Ensuring a smooth transition 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 calling LLM APIs or developing prompt-based features
- Foundational understanding 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 implementations
- Platform engineers supporting production AI workloads
