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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer Role
- Distinguishing this role from ML engineers and solutions engineers
- Focusing on outcome ownership rather than just delivery milestones
- Collaborating with product, research, and security teams
Discovery in Complex Enterprise Environments
- Observing end-to-end workflows through shadowing
- Mapping existing systems, data flows, and handoff points
- Identifying key constraints that hinder value creation
Problem Scoping and Prioritisation
- Qualifying use cases for technical feasibility
- Scoring candidate workflows based on required effort
- Selecting the initial workflow to validate
Value Framing and Acceptance Criteria
- Establishing baseline metrics prior to development
- Articulating business cases for financial stakeholders
- Defining acceptance criteria in collaboration with users
Model Selection: Frontier and Open Weights
- Comparing GPT-6, Claude 5.5, and open-source models
- Balancing context window, latency, and unit costs
- Determining when data governance mandates locally hosted models
Context Engineering and Prompt Design
- Structuring instructions to ensure reliable behaviour
- Aggregating context from primary source systems
- Optimising prompt caching and minimising token usage
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content effectively
- Implementing hybrid search, reranking, and citation mechanisms
- Evaluating retrieval performance independently from generation
Agents and Tool Usage
- Designing tools and defining typed interfaces
- Building with OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure scenarios
MCP and Enterprise Integration
- Understanding the Model Context Protocol revision of 2026-07-28
- Developing and securing MCP servers
- Aligning tool permissions with enterprise identity standards
Evaluation as the Core of Delivery
- Constructing golden datasets from actual work samples
- Using 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 data handling responsibilities
Deployment Architecture in Client Environments
- Navigating identity, network, and data residency requirements
- Utilising model gateways and multi-provider abstraction layers
- Applying infrastructure as code and standardised release processes
Observability, Cost, and Token Economics
- Tracing agent executions and tool invocations
- Creating dashboards for quality monitoring and spending analysis
- Reducing costs without compromising output quality
Adoption and Change Management
- Empowering end-users involved in daily operations
- Building champion networks and establishing feedback loops
- Tracking and measuring adoption speed
From Engagement to Reusable Capability
- Packaging proven workflows for reuse
- Developing playbooks, blueprints, and accelerators
- Facilitating smooth handovers to internal teams
Requirements
- Proficiency in Python, including experience with APIs and virtual environments
- Familiarity with cloud platforms, containerisation, and version control systems
- Previous experience integrating 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 overseeing AI implementations
- Platform engineers managing production AI workloads
