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 this role from ML engineers and solutions engineers
- Focusing on business outcomes rather than mere delivery milestones
- Collaborating effectively with product, research, and security teams
Discovery Within Complex Enterprises
- Observing workflows from start to finish
- Mapping systems, data flows, and handoff points
- Identifying the key constraint limiting value creation
Problem Scoping and Prioritisation
- Assessing use cases for technical feasibility
- Scoring potential workflows based on required effort
- Selecting the initial workflow to validate
Value Framing and Acceptance Criteria
- Establishing baseline metrics prior to development
- Using business-appropriate language for finance stakeholders
- Agreeing on acceptance criteria with end users
Model Selection: Frontier vs Open Weights
- Comparing GPT-6, Claude 5.5, and open-source models
- Balancing context, latency, and unit cost trade-offs
- Determining when data regulations mandate locally hosted models
Context Engineering and Prompt Design
- Structuring instructions for consistent, reliable behaviour
- Gathering context from enterprise systems of record
- Implementing prompt caching and token efficiency strategies
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content effectively
- Utilising hybrid search, reranking, and citation techniques
- Assessing retrieval performance independently of generation
Agents and Tool Utilisation
- Designing tools with typed interfaces
- Developing with the OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure scenarios
MCP and Enterprise Integration
- Understanding the Model Context Protocol 2026-07-28 revision
- Building and securing MCP servers
- Enforcing tool permissions against enterprise identity systems
Evaluation as the Core Delivery Language
- Constructing golden sets from real-world work
- Implementing automated scoring and LLM-based judges
- Setting release gates based on evaluation thresholds
Guardrails, Safety, and Human Oversight
- Implementing guardrails at the prompt, tool, and policy levels
- Defining escalation paths and human review checkpoints
- Maintaining audit trails and adhering to data handling obligations
Deployment Architecture Within the Client Estate
- Navigating identity, network, and data residency constraints
- Using model gateways and multi-provider abstractions
- Applying infrastructure as code and streamlined release processes
Observability, Cost, and Token Economics
- Tracing agent execution and tool interactions
- Creating dashboards for quality and expenditure monitoring
- Optimising costs while maintaining service quality
Adoption and Change Management
- Enabling the staff performing the actual work
- Building champion networks and feedback loops
- Measuring the velocity of user adoption
From Engagement to Reusable Capability
- Packaging proven workflows for reuse
- Developing playbooks, blueprints, and accelerators
- Facilitating handover to internal teams
Requirements
- Proficiency in Python, including experience with APIs and virtual environments
- Familiarity with cloud platforms, containerisation, and version control systems
- Prior experience calling 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 client-facing AI delivery roles
- Technical consultants and solutions architects responsible for AI implementation
- Platform engineers supporting production AI workloads
