AI Forward Deployed Engineer: Scoping, Building and Shipping Production AI Systems Training Course

The Role of the AI Forward Deployed Engineer

• Distinguishing this role from ML Engineers and Solutions Engineers
• Focusing on business outcomes rather than mere delivery milestones
• Collaborating effectively with product, research, and security teams

Discovery in Complex Enterprise Environments

• Observing workflows from start to finish
• Mapping out systems, data flows, and handoff points
• Identifying constraints that hinder value delivery

Problem Definition and Prioritisation

• Evaluating use cases for technical feasibility
• Scoring potential workflows based on effort and impact
• Selecting the initial workflow to validate

Value Articulation and Acceptance Criteria

• Establishing baseline metrics prior to development
• Using business-case language suitable for finance 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 context window, latency, and unit costs
• Scenarios where data regulations necessitate locally hosted models

Context Engineering and Prompt Architecture

• Structuring instructions to ensure consistent behaviour
• Aggregating context from authoritative source systems
• Implementing prompt caching and token optimisation

Retrieval and Knowledge Grounding

• Segmenting and indexing enterprise content
• Utilising hybrid search, reranking, and citation mechanisms
• Evaluating retrieval performance independently of generation

Agents and Tool Integration

• Designing tools and defining typed interfaces
• Developing with OpenAI and Claude agent SDKs
• Managing approvals, retries, and error handling

MCP and Enterprise Integration

• Understanding the Model Context Protocol 2026-07-28 revision
• Developing and securing MCP servers
• Aligning tool permissions with enterprise identity frameworks

Evaluation as the Core Delivery Standard

• Creating golden datasets derived from real-world tasks
• 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
• Managing audit trails and data handling responsibilities

Deployment Architecture within Client Infrastructure

• Navigating identity, network, and data residency restrictions
• Using model gateways and multi-provider abstraction layers
• Applying Infrastructure as Code and structured release processes

Observability, Cost Management, and Token Economics

• Tracing agent executions and tool invocations
• Creating dashboards for quality monitoring and expenditure tracking
• Reducing costs without compromising output quality

Adoption and Change Management

• Empowering the teams responsible for daily operations
• Building champion networks and establishing feedback loops
• Tracking adoption speed and usage metrics

From Project Delivery to Reusable Assets

• Packaging proven workflows for broader use
• Creating playbooks, blueprints, and accelerators
• Transferring ownership to internal teams