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Duration 28 hours
Course Outline
The Role of the AI Forward Deployed Engineer
- Differentiating this role from ML engineers and solutions engineers
- Focusing on outcomes rather than merely hitting delivery milestones
- Collaborating 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 constraints that impede value creation
Problem Definition and Prioritization
- Evaluating use cases for feasibility
- Scoring potential workflows based on effort required
- Selecting the initial workflow for validation
Value Proposition and Success Metrics
- Establishing baseline metrics prior to development
- Articulating the business case for financial stakeholders
- Defining acceptance criteria in collaboration with users
Model Selection: Frontier and Open-Weight Options
- Comparing GPT-6, Claude 5.5, and open-source models
- Balancing context, latency, and unit cost trade-offs
- Situations where data regulations require locally hosted models
Context Engineering and Prompt Architecture
- Structuring instructions for consistent performance
- Integrating context from authoritative data sources
- Implementing prompt caching and token optimization
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content
- Utilizing hybrid search, reranking, and citation techniques
- Assessing retrieval performance independently from generation
Agents and Tool Utilization
- Designing tools and typed interfaces
- Development using OpenAI and Claude agent SDKs
- Managing approvals, retries, and error handling
MCP and Enterprise Integration
- Overview of the July 28, 2026 Model Context Protocol revision
- Creating and securing MCP servers
- Aligning tool permissions with enterprise identity systems
Evaluation as the Standard for Delivery
- Creating golden sets from real-world scenarios
- Implementing automated scoring and LLM-based judging
- Linking release gates to 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 complying with data handling obligations
Deployment Architecture within Client Environments
- Navigating identity, network, and data residency restrictions
- Utilizing model gateways and multi-provider abstraction
- Applying infrastructure as code and release procedures
Observability, Cost Management, and Token Economics
- Tracing agent executions and tool invocations
- Creating dashboards for quality monitoring and expenditure tracking
- Reducing costs while maintaining quality standards
Adoption and Change Management
- Enabling staff involved in daily operations
- Building champion networks and feedback mechanisms
- Tracking adoption speed
Transitioning from Projects to Reusable Assets
- Standardizing proven workflows
- Developing playbooks, blueprints, and accelerators
- Transferring ownership to internal teams
Requirements
- Proficiency in Python, including API integration and virtual environment management
- Knowledge of cloud platforms, containerization, and version control
- Previous experience calling LLM APIs or developing prompt-based features
- Fundamental understanding 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
- Technical consultants and solutions architects overseeing AI implementation
- Platform engineers supporting production AI workloads
