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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer
- Differentiating the role from ML and solutions engineers
- Focusing on outcomes rather than just delivery milestones
- Collaborating with product, research, and security teams
Discovery in Complex Enterprise Environments
- Observing workflows end-to-end
- Mapping systems, data flows, and handoff points
- Identifying the primary constraint limiting value
Problem Scoping and Prioritization
- Assessing use cases for technical feasibility
- Evaluating candidate workflows against implementation effort
- Selecting the initial workflow for proof-of-concept
Value Framing and Acceptance Criteria
- Establishing baseline metrics before development
- Using business case language suitable for finance sponsors
- Defining acceptance criteria with end-users
Model Selection: Frontier and Open Weights
- Comparing GPT-6, Claude 5.5, and open-source models
- Balancing trade-offs in context, latency, and unit cost
- Implementing locally hosted models when data regulations require
Context Engineering and Prompt Design
- Structuring instructions to ensure reliable behavior
- Assembling context from systems of record
- Utilizing prompt caching and minimizing token usage
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content
- Implementing hybrid search, reranking, and citation features
- Evaluating retrieval performance independently of generation
Agents and Tool Use
- Designing tools with typed interfaces
- Building applications using OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure handling
MCP and Enterprise Integration
- Applying the Model Context Protocol revision of July 28, 2026
- Developing and securing MCP servers
- Linking tool permissions to enterprise identity systems
Evaluation as the Delivery Standard
- Creating golden sets from real-world tasks
- Implementing automated scoring and LLM-based judges
- Setting release gates based on evaluation thresholds
Guardrails, Safety, and Human Oversight
- Applying guardrails at prompt, tool, and policy levels
- Defining escalation paths and human review points
- Maintaining audit trails and managing data handling responsibilities
Deployment Architecture in the Customer Environment
- Addressing identity, network, and data residency constraints
- Using model gateways and multi-provider abstractions
- Managing infrastructure as code and release processes
Observability, Cost, and Token Economics
- Tracing agent executions and tool calls
- Creating dashboards for quality and expenditure
- Reducing costs while maintaining quality
Adoption and Change Management
- Enabling the teams responsible for the work
- Building champion networks and feedback loops
- Measuring adoption speed
From Engagement to Reusable Capability
- Packaging proven workflows
- Developing playbooks, blueprints, and accelerators
- Transitioning ownership to internal teams
Requirements
- Proficiency in Python, including experience with APIs and virtual environments
- Familiarity with cloud platforms, containers, and version control
- Previous experience calling LLM APIs or building prompt-based features
- Basic understanding of enterprise systems such as CRM, ERP, or ticketing platforms
Audience
- Forward deployed and applied AI engineers
- Software engineers transitioning into customer-facing AI delivery
- Technical consultants and solutions architects responsible for AI implementation
- Platform engineers supporting production AI workloads
