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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer Role
- Distinguishing the role from ML Engineer and Solutions Engineer positions.
- Focusing on business outcomes rather than delivery milestones.
- Collaborating effectively with product, research, and security teams.
Discovery in Complex Enterprise Environments
- Observing workflows from start to finish.
- Mapping systems, data flows, and handoff points.
- Identifying key constraints that hinder value creation.
Problem Scoping and Prioritization
- Qualifying use cases based on feasibility.
- Evaluating candidate workflows based on effort and impact.
- Selecting the initial workflow for proof of concept.
Value Framing and Acceptance Criteria
- Establishing baseline metrics before development begins.
- Using business-friendly language for financial stakeholders.
- Defining acceptance criteria in collaboration with end users.
Model Selection: Frontier and Open-Weight Models
- Comparing GPT-6, Claude 5.5, and open-source alternatives.
- Balancing context window, latency, and unit cost.
- Determining when data policies necessitate locally hosted models.
Context Engineering and Prompt Design
- Structuring instructions to ensure consistent model behavior.
- Gathering context from primary systems of record.
- Optimizing for prompt caching and token efficiency.
Retrieval and Knowledge Grounding
- Segmenting and indexing enterprise content.
- Implementing hybrid search, reranking, and citation strategies.
- Assessing retrieval accuracy independently of generation quality.
Agents and Tool Utilization
- Designing tools with typed interfaces.
- Developing using OpenAI and Claude agent SDKs.
- Managing approvals, retries, and error handling.
MCP and Enterprise Integration
- Understanding the Model Context Protocol (MCP) 2026-07-28 revision.
- Developing and securing MCP servers.
- Aligning tool permissions with enterprise identity management.
Evaluation as a Delivery Metric
- Creating golden test sets from real-world work scenarios.
- Using automated scoring and LLM-based judges.
- Establishing 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 data handling compliance.
Deployment Architecture within Client Environments
- Addressing identity, network, and data residency requirements.
- Implementing model gateways and multi-provider abstractions.
- Managing infrastructure as code and release processes.
Observability, Cost, and Token Economics
- Tracing agent executions and tool invocations.
- Monitoring quality and expenditure via dashboards.
- Reducing costs without compromising output quality.
Adoption and Change Management
- Empowering the teams executing the work.
- Building champion networks and feedback loops.
- Tracking adoption velocity and progress.
From Single Engagement to Reusable Capability
- Encapsulating proven workflows.
- Creating playbooks, blueprints, and accelerators.
- Facilitating handover to internal teams.
Requirements
- Proficiency in Python, including API integration and virtual environment management.
- Familiarity with cloud platforms, containerization, and version control systems.
- Previous experience interacting with LLM APIs or developing prompt-based features.
- Foundational knowledge of enterprise systems such as CRM, ERP, or ticketing platforms.
Target Audience
- Forward deployed and applied AI engineers.
- Software engineers transitioning into client-facing AI delivery roles.
- Technical consultants and solution architects leading AI deployments.
- Platform engineers supporting production AI workloads.
