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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer Role
- Differentiating this role from ML Engineer and Solutions Engineer positions
- Focusing on business outcomes rather than just delivery milestones
- Collaborating effectively with product, research, and security teams
Discovery in Complex Enterprise Environments
- Observing workflows end-to-end through shadowing
- Mapping existing systems, data flows, and handoff points
- Identifying key constraints that hinder value creation
Problem Scoping and Prioritization
- Qualifying use cases based on technical feasibility
- Scoring candidate workflows against resource effort
- Selecting the initial workflow to validate
Value Framing and Acceptance Criteria
- Establishing baseline metrics prior to development
- Communicating the business case effectively to finance sponsors
- Defining acceptance criteria in collaboration with end-users
Model Selection: Frontier and Open-Weight Options
- Comparing capabilities of GPT-6, Claude 5.5, and open-source models
- Weighing trade-offs between context window, latency, and unit cost
- Determining when data regulations require locally hosted models
Context Engineering and Prompt Design
- Structuring instructions to ensure reliable model behavior
- Aggregating context from systems of record
- Optimizing through prompt caching and token minimization
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content effectively
- Implementing hybrid search, reranking, and citation strategies
- Evaluating retrieval quality independently from generation
Agents and Tool Integration
- Designing tools and typed interfaces for AI agents
- Building solutions using OpenAI and Claude agent SDKs
- Handling approvals, retries, and failure scenarios
MCP and Enterprise Integration
- Exploring the Model Context Protocol (2026-07-28 revision)
- Developing and securing MCP servers
- Aligning tool permissions with enterprise identity management
Evaluation as the Delivery Standard
- Creating golden sets derived from real-world work
- Utilizing automated scoring and LLM-based judges
- Establishing release gates based on evaluation thresholds
Guardrails, Safety, and Human Oversight
- Implementing guardrails at the prompt, tool, and policy layers
- Defining escalation paths and human review checkpoints
- Managing audit trails and data handling responsibilities
Deployment Architecture in Client Environments
- Navigating identity, network, and data residency constraints
- Using model gateways and multi-provider abstraction
- Applying infrastructure as code and standardized release processes
Observability, Cost, and Token Economics
- Tracing agent executions and tool invocations
- Creating dashboards for quality monitoring and spend tracking
- Reducing costs while maintaining service quality
Adoption and Change Management
- Enabling the teams responsible for daily operations
- Building champion networks and effective feedback loops
- Measuring the velocity of adoption
From Project Engagement to Reusable Capability
- Packaging validated workflows for broader use
- Creating playbooks, blueprints, and accelerators
- 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 interacting with LLM APIs or developing prompt-based features
- Foundational 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 roles
- Technical consultants and solutions architects leading AI implementations
- Platform engineers managing production AI workloads
