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

The AI Forward Deployed Engineer

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

Discovery in Complex Enterprise Environments

• Tracing workflows from start to finish
• Mapping out systems, data flows, and handoff points
• Identifying the specific constraints that hinder value creation

Problem Scoping and Prioritization

• Validating use cases for technical feasibility
• Evaluating candidate workflows based on effort versus impact
• Selecting the initial workflow for validation

Value Proposition and Acceptance Criteria

• Establishing baseline metrics before development begins
• Communicating the business case in language relevant to finance sponsors
• 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 trade-offs between context window, latency, and unit costs
• Determining when data regulations necessitate locally hosted models

Context Engineering and Prompt Design

• Structuring instructions to ensure consistent, reliable behavior
• Aggregating context from authoritative systems of record
• Optimizing performance through prompt caching and token efficiency

Retrieval and Knowledge Grounding

• Segmenting and indexing enterprise content for retrieval
• Implementing hybrid search, reranking, and citation mechanisms
• Assessing retrieval quality independently from generation quality

Agents and Tool Integration

• Designing tools with well-defined, typed interfaces
• Developing agents using OpenAI and Claude agent SDKs
• Managing approvals, retries, and error handling

MCP and Enterprise Integration

• Utilizing the 2026-07-28 Model Context Protocol revision
• Creating and securing MCP servers
• Aligning tool permissions with enterprise identity management

Evaluation as the Standard for Delivery

• Constructing golden datasets derived from real-world tasks
• Implementing automated scoring and LLM-based judging
• Establishing release gates linked to evaluation thresholds

Guardrails, Safety, and Human Oversight

• Applying guardrails across prompt, tool, and policy layers
• Defining escalation paths and human review checkpoints
• Maintaining audit trails and adhering to data handling responsibilities

Deployment Architecture within the Client Ecosystem

• Navigating identity, network, and data residency restrictions
• Implementing model gateways and multi-provider abstractions
• Using Infrastructure as Code and standardized release processes

Observability, Cost Management, and Token Economics

• Tracing agent executions and tool invocations
• Creating dashboards for monitoring quality and expenditure
• Reducing costs while preserving system quality

Adoption and Organizational Change

• Empowering the workforce involved in the workflow
• Building champion networks and continuous feedback loops
• Tracking adoption velocity and metrics

Transitioning from Engagement to Reusable Capability

• Standardizing and packaging proven workflows
• Creating playbooks, blueprints, and development accelerators
• Facilitating handover to internal client teams