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Duration 28 hours
Course Outline
The Role of the AI Forward Deployed Engineer
- Distinguishing this role from ML engineers and solutions engineers
- Focusing on outcome ownership rather than mere delivery milestones
- Collaborating with product, research, and security teams
Discovery Within Complex Enterprises
- Observing workflows end-to-end
- Mapping systems, data flows, and handoff points
- Identifying the primary constraints limiting value generation
Problem Definition and Prioritisation
- Assessing use cases for practical feasibility
- Ranking candidate workflows based on effort required
- Selecting the initial workflow for proof-of-concept
Value Proposition and Acceptance Criteria
- Establishing baseline metrics before development begins
- Using financial language to support the business case
- Agreeing on acceptance criteria with end-users
Model Selection: Frontier vs. Open Weights
- Comparing GPT-6, Claude 5.5, and open-source models
- Balancing context length, latency, and unit costs
- Determining when data governance requires locally hosted models
Context Engineering and Prompt Design
- Structuring instructions for consistent behaviour
- Assembling context from systems of record
- Utilising prompt caching and minimising token usage
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content
- Implementing hybrid search, reranking, and citation
- Evaluating retrieval performance independently from generation
Agents and Tool Integration
- Designing tools and typed interfaces
- Building with OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure recovery
MCP and Enterprise Integration
- Understanding the Model Context Protocol 2026-07-28 revision
- Developing and securing MCP servers
- Aligning tool permissions with enterprise identity management
Evaluation as the Standard for Delivery
- Creating golden sets from real-world work
- Implementing automated scoring and LLM-based judging
- Establishing release gates tied to evaluation thresholds
Guardrails, Safety, and Human Oversight
- Applying guardrails at prompt, tool, and policy levels
- Defining escalation paths and human review checkpoints
- Maintaining audit trails and adhering to data handling responsibilities
Deployment Architecture within the Client Environment
- Navigating identity, network, and data residency requirements
- Using model gateways and multi-provider abstractions
- Implementing infrastructure as code and structured release processes
Observability, Cost, and Token Economics
- Tracing agent executions and tool calls
- Creating dashboards for quality and spending monitoring
- Reducing costs while maintaining output quality
Adoption and Change Management
- Enabling the individuals performing the work
- Building champion networks and feedback loops
- Measuring the speed of adoption
Transitioning from Engagement to Reusable Capability
- Packaging proven workflows for reuse
- Developing playbooks, blueprints, and accelerators
- Handing over to internal teams
Requirements
- Strong proficiency in Python, including API interaction and virtual environment management
- Experience with cloud platforms, containerisation, and version control systems
- Previous experience interacting with LLM APIs or developing prompt-based functionalities
- Fundamental 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
- Technical consultants and solution architects leading AI implementations
- Platform engineers supporting live AI workloads
