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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer Role
- Distinguishing this role from ML Engineer and Solutions Engineer responsibilities
- Focusing on end results rather than mere delivery milestones
- Collaborating effectively with product, research, and security teams
Discovery in Complex Enterprises
- Observing workflows from start to finish
- Mapping out systems, data flows, and handoff points
- Identifying the key constraints limiting value delivery
Defining Scope and Prioritization
- Assessing use cases for practical feasibility
- Ranking potential workflows based on implementation effort
- Selecting the initial workflow for proof-of-concept
Value Proposition and Success Metrics
- Establishing baseline metrics prior to development
- Articulating the business case for financial stakeholders
- Defining acceptance criteria in collaboration with end users
Model Selection: Frontier and Open-Source
- Comparing GPT-6, Claude 5.5, and open-weight models
- Balancing trade-offs in context window, latency, and unit cost
- Scenarios requiring locally hosted models due to data regulations
Context Engineering and Prompt Strategy
- Structuring instructions to ensure consistent model behavior
- Integrating context from official systems of record
- Optimizing via prompt caching and token reduction
Retrieval and Knowledge Grounding
- Segmenting and indexing enterprise content
- Implementing hybrid search, reranking, and citation mechanisms
- Assessing retrieval performance independently from generation
Agents and Tool Integration
- Designing tools and defining typed interfaces
- Developing using OpenAI and Claude Agent SDKs
- Managing approvals, retries, and error handling
MCP and Enterprise Integration
- Reviewing the Model Context Protocol revision (2026-07-28)
- Building and securing MCP servers
- Enforcing tool permissions through enterprise identity systems
Evaluation as the Core Delivery Method
- Creating golden datasets from real-world tasks
- Utilizing automated scoring and LLM-based judges
- Establishing release gates linked 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 adhering to data handling standards
Deployment Architecture in Client Environments
- Addressing identity, network, and data residency requirements
- Using model gateways and multi-provider abstraction layers
- Applying infrastructure as code and structured release processes
Observability, Cost, and Token Economics
- Tracing agent executions and tool invocations
- Monitoring quality and expenditure via dashboards
- Reducing costs while maintaining output quality
Adoption and Change Management
- Training and enabling the workforce using the new tools
- Cultivating champion networks and continuous feedback loops
- Tracking the speed and scale of user adoption
Transitioning from Projects to Reusable Assets
- Encapsulating proven workflows into modular components
- Developing playbooks, blueprints, and accelerators
- Facilitating smooth handover to internal client 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 invoking LLM APIs or developing prompt-driven features
- Fundamental understanding of enterprise platforms such as CRM, ERP, or ticketing systems
Target Audience
- Forward Deployed and Applied AI Engineers
- Software Engineers transitioning into customer-facing AI delivery roles
- Technical Consultants and Solutions Architects leading AI implementations
- Platform Engineers supporting production AI workloads
