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

The AI Forward Deployed Engineer

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

Discovery in Complex Enterprises

• Tracing workflows from start to finish.
• Mapping systems, data, and handoffs.
• Identifying the constraints that hinder value delivery.

Problem Scoping and Prioritization

• Qualifying use cases for feasibility.
• Scoring candidate workflows based on effort.
• Selecting the initial workflow to validate.

Value Framing and Acceptance Criteria

• Establishing baseline metrics before development.
• Using business case language for financial sponsors.
• Defining acceptance criteria with users.

Model Selection: Frontier and Open Weights

• Comparing GPT-6, Claude 5.5, and open models.
• Balancing context, latency, and unit cost.
• Determining when data regulations require locally hosted models.

Context Engineering and Prompt Design

• Structuring instructions for consistent behavior.
• Assembling context from systems of record.
• Implementing prompt caching and token optimization.

Retrieval and Knowledge Grounding

• Chunking and indexing enterprise content.
• Using hybrid search, reranking, and citation.
• Evaluating retrieval independently of generation.

Agents and Tool Use

• Designing tools and typed interfaces.
• Building with OpenAI and Claude agent SDKs.
• Handling approvals, retries, and failures.

MCP and Enterprise Integration

• The Model Context Protocol revision of July 28, 2026.
• Building and securing MCP servers.
• Aligning tool permissions with enterprise identity.

Evaluation as the Delivery Language

• Creating golden sets from real-world work.
• Using automated scoring and LLM judges.
• Linking release gates to evaluation thresholds.

Guardrails, Safety, and Human Oversight

• Implementing guardrails at prompt, tool, and policy levels.
• Defining escalation and human review points.
• Managing audit trails and data handling responsibilities.

Deployment Architecture in the Customer Estate

• Addressing identity, network, and data residency constraints.
• Using model gateways and multi-provider abstractions.
• Applying infrastructure as code and release processes.

Observability, Cost, and Token Economics

• Tracing agent runs and tool calls.
• Creating dashboards for quality and spending.
• Reducing costs without compromising quality.

Adoption and Change

• Enabling staff performing the work.
• Building champion networks and feedback loops.
• Measuring adoption speed.

From Engagement to Reusable Capability

• Packaging proven workflows.
• Developing playbooks, blueprints, and accelerators.
• Transferring knowledge to internal teams.