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

The AI Forward Deployed Engineer

• Distinguishing the role from ML engineers and solutions engineers
• Focusing on outcomes rather than delivery milestones
• Collaborating with product, research, and security teams

Discovery in Complex Enterprises

• Observing workflows from start to finish
• Mapping systems, data flows, and handoff points
• Identifying the primary constraint hindering value creation

Problem Scoping and Prioritization

• Qualifying use cases for technical feasibility
• Scoring candidate workflows against implementation effort
• Selecting the initial workflow for proof of concept

Value Framing and Acceptance Criteria

• Establishing baseline metrics before development
• Articulating business cases for finance stakeholders
• Defining acceptance criteria in consultation 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 cost
• Determining when data governance mandates locally hosted models

Context Engineering and Prompt Design

• Structuring instructions to ensure reliable behaviour
• Assembling context from systems of record
• Implementing prompt caching and token minimization

Retrieval and Knowledge Grounding

• Chunking and indexing enterprise content
• Applying hybrid search, reranking, and citation strategies
• Evaluating retrieval performance independently of generation

Agents and Tool Utilization

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

MCP and Enterprise Integration

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

Evaluation as the Delivery Standard

• Constructing golden datasets from real-world work
• Using automated scoring and LLM-based judging
• Setting release gates based on 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 adhering to data handling obligations

Deployment Architecture within the Client Estate

• Navigating identity, network, and data residency constraints
• Utilizing model gateways and multi-provider abstraction
• Applying infrastructure as code and structured release processes

Observability, Cost, and Token Economics

• Tracing agent executions and tool interactions
• Creating dashboards for quality monitoring and expenditure tracking
• Reducing costs while maintaining output quality

Adoption and Change Management

• Empowering the individuals performing the work
• Building champion networks and feedback mechanisms
• Measuring adoption speed and effectiveness

Transitioning from Engagement to Reusable Capability

• Packaging validated workflows
• Developing playbooks, blueprints, and accelerators
• Transferring ownership to internal teams