Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 28 hours
Course Outline
The AI Forward Deployed Engineer Role
- Distinguishing the role from ML engineers and solutions engineers
- Focusing on owning business outcomes rather than just delivery milestones
- Collaborating with product, research, and security teams
Discovery in Complex Enterprise Environments
- Observing workflows end-to-end to understand processes
- Mapping out systems, data flows, and handoff points
- Identifying the primary constraints that hinder value creation
Problem Scoping and Prioritization
- Validating use cases for technical feasibility
- Evaluating potential workflows based on effort and impact
- Selecting the initial workflow to pilot and prove value
Value Framing and Acceptance Criteria
- Establishing baseline metrics before development begins
- Communicating business case language to finance stakeholders
- Defining acceptance criteria in collaboration with end-users
Model Selection: Frontier and Open-Weight Options
- Comparing GPT-6, Claude 5.5, and open-source models
- Balancing context window, latency, and unit cost trade-offs
- Determining when data regulations necessitate locally hosted models
Context Engineering and Prompt Design
- Structuring instructions to ensure reliable model behavior
- Gathering context from authoritative systems of record
- Optimizing via prompt caching and token minimization
Retrieval and Knowledge Grounding
- Segmenting and indexing enterprise content for retrieval
- Implementing hybrid search, reranking, and citation mechanisms
- Assessing retrieval performance independently from generation quality
Agents and Tool Integration
- Designing tools and defining typed interfaces
- Building solutions using OpenAI and Claude agent SDKs
- Managing approvals, retries, and error handling
MCP and Enterprise Integration
- Understanding the Model Context Protocol 2026-07-28 revision
- Developing and securing MCP servers
- Configuring tool permissions aligned with enterprise identity standards
Evaluation as the Standard for Delivery
- Creating golden test sets derived from real-world work
- 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 responsibilities
Deployment Architecture within Client Infrastructure
- Navigating identity, network, and data residency requirements
- Implementing model gateways and multi-provider abstractions
- Managing infrastructure as code and release processes
Observability, Cost Management, and Token Economics
- Tracing agent executions and tool invocations
- Creating dashboards for quality monitoring and expenditure tracking
- Reducing costs while maintaining service quality
Adoption and Change Management
- Enabling end-users who perform the daily work
- Establishing champion networks and feedback loops
- Measuring the speed and effectiveness of adoption
Transitioning from Projects to Reusable Capabilities
- Packaging validated workflows for reuse
- Developing playbooks, blueprints, and accelerators
- Ensuring smooth handover to internal client teams
Requirements
- Proficiency in Python, including experience with APIs and virtual environments
- Familiarity with cloud platforms, containers, and version control systems
- Prior experience interacting with LLM APIs or developing prompt-based features
- Basic 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
- Technical consultants and solutions architects responsible for AI deployments
- Platform engineers supporting production AI workloads
