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

The Role of the AI Forward Deployed Engineer

• Distinguishing the role from that of ML engineers and solutions engineers
• Focusing on achieving business outcomes rather than merely meeting delivery milestones
• Collaborating effectively with product, research, and security teams

Discovery in Complex Enterprise Environments

• Observing and mapping workflows from start to finish
• Documenting systems, data flows, and handoff points
• Identifying critical constraints that hinder value creation

Problem Scoping and Prioritisation

• Assessing use cases for technical and business feasibility
• Ranking potential workflows based on implementation effort
• Selecting the initial workflow to validate and prove

Value Framing and Acceptance Criteria

• Establishing performance baselines prior to development
• Articulating the business case in terms relevant to financial stakeholders
• Defining clear acceptance criteria in collaboration with end-users

Model Selection: Frontier and Open-Weight Models

• Comparing capabilities and constraints of GPT-6, Claude 5.5, and open-source models
• Balancing trade-offs among context window, latency, and unit cost
• Determining when data sovereignty rules necessitate locally hosted models

Context Engineering and Prompt Design

• Structuring prompts to ensure consistent and reliable model behaviour
• Aggregating relevant context from systems of record
• Optimising performance through prompt caching and token reduction

Retrieval and Knowledge Grounding

• Segmenting and indexing enterprise content effectively
• Implementing hybrid search, reranking strategies, and citation mechanisms
• Evaluating retrieval accuracy independently of text generation quality

Agents and Tool Utilisation

• Designing tools with strongly typed interfaces
• Developing agent solutions using OpenAI and Claude agent SDKs
• Managing approval workflows, retry logic, and error handling

MCP and Enterprise Integration

• Implementing the Model Context Protocol revision of 2026-07-28
• Building secure MCP servers
• Aligning tool permissions with enterprise identity management

Evaluation as a Core Delivery Metric

• Creating golden datasets derived from real-world tasks
• Utilising automated scoring and LLM-based judging mechanisms
• Establishing release gates linked to specific evaluation thresholds

Guardrails, Safety, and Human Oversight

• Implementing safeguards 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 Client Infrastructure

• Navigating constraints related to identity, network security, and data residency
• Utilising model gateways and multi-provider abstraction layers
• Adopting infrastructure as code and structured release processes

Observability, Cost Management, and Token Economics

• Tracing agent executions and individual tool calls
• Monitoring quality metrics and expenditure through dashboards
• Reducing operational costs without compromising output quality

Adoption and Change Management

• Enabling the workforce responsible for executing AI-assisted tasks
• Cultivating champion networks and establishing feedback loops
• Tracking and measuring the speed of adoption

Transitioning from Engagement to Reusable Capability

• Packaging validated workflows for broader use
• Creating standardised playbooks, blueprints, and accelerators
• Facilitating smooth handover to internal client teams