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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer
- Differentiating the role from ML engineers and solution engineers
- Focusing on outcomes rather than just delivery milestones
- Collaborating effectively with product, research, and security teams
Discovery in Complex Enterprise Environments
- Observing end-to-end workflows through shadowing
- Mapping systems, data flows, and handoff points
- Identifying constraints that hinder value creation
Problem Scoping and Prioritization
- Validating use cases for technical feasibility
- Evaluating candidate workflows against implementation effort
- Selecting the initial workflow to demonstrate value
Value Definition and Acceptance Criteria
- Establishing metric baselines before development begins
- Communicating the business case to financial stakeholders
- Aligning with users on specific acceptance criteria
Model Selection: Frontier and Open-Weight Options
- Benchmarking GPT-6, Claude 5.5, and open-source models
- Balancing trade-offs in context length, latency, and unit costs
- Determining when data regulations require locally hosted models
Context Engineering and Prompt Design
- Structuring instructions to ensure consistent behavior
- Aggregating context from core systems of record
- Optimizing for prompt caching and token efficiency
Retrieval and Knowledge Grounding
- Processing and indexing enterprise content effectively
- Implementing hybrid search, reranking, and citation mechanisms
- Assessing retrieval performance independently of generation
Agents and Tool Integration
- Designing robust tools and typed interfaces
- Developing using the OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure scenarios
MCP and Enterprise Integration
- Understanding the Model Context Protocol revision from 2026-07-28
- Building and securing MCP servers
- Enforcing tool permissions via enterprise identity systems
Evaluation as the Primary Delivery Metric
- Creating golden test sets based on real-world work
- Implementing automated scoring and LLM-based judging
- Defining release gates based on evaluation thresholds
Guardrails, Safety, and Human Oversight
- Implementing safeguards at the prompt, tool, and policy levels
- Establishing escalation paths and human review checkpoints
- Maintaining audit trails and complying with data handling obligations
Deployment Architecture in Client Environments
- Addressing identity, network, and data residency requirements
- Utilizing model gateways and multi-provider abstractions
- Applying infrastructure as code and structured release processes
Observability, Cost, and Token Economics
- Tracking agent executions and tool invocations
- Monitoring quality and expenditure via dashboards
- Reducing costs without compromising output quality
Adoption and Organizational Change
- Empowering end-users to effectively utilize the tools
- Cultivating champion networks and continuous feedback loops
- Tracking adoption speed and user engagement
Transitioning from Projects to Reusable Capabilities
- Standardizing proven workflows into packages
- Creating playbooks, blueprints, and acceleration tools
- Facilitating smooth handovers to internal teams
Requirements
- Strong proficiency in Python, including API integration and virtual environment management
- Working knowledge of cloud platforms, containerization, and version control systems
- 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 customer-facing AI delivery roles
- Technical consultants and solution architects overseeing AI implementations
- Platform engineers supporting production AI workloads
