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
- Distinguishing the role from ML engineer and solutions engineer
- Focusing on owning outcomes rather than just delivery milestones
- Collaborating with product, research, and security teams
Discovery in Complex Enterprises
- Observing workflows from start to finish
- Mapping systems, data flows, and handoff points
- Identifying the specific constraints blocking value delivery
Problem Scoping and Prioritisation
- Qualifying use cases for technical feasibility
- Scoring candidate workflows based on effort required
- Selecting the first workflow to validate
Value Framing and Acceptance Criteria
- Establishing baseline metrics before development begins
- Using business-case language to communicate with finance sponsors
- Defining acceptance criteria in collaboration with end-users
Model Selection: Frontier and Open Weights
- Comparing GPT-6, Claude 5.5, and open-source models
- Balancing context window, latency, and unit cost trade-offs
- Determining when data regulations require locally hosted models
Context Engineering and Prompt Design
- Structuring instructions to ensure reliable model behaviour
- Assembling context from authoritative systems of record
- Utilising prompt caching and minimising token usage
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content effectively
- Implementing hybrid search, reranking, and citation mechanisms
- Evaluating retrieval performance independently from generation
Agents and Tool Use
- Designing tools with typed interfaces
- Building solutions using OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure handling
MCP and Enterprise Integration
- Understanding the Model Context Protocol 2026-07-28 revision
- Building and securing MCP servers
- Aligning tool permissions with enterprise identity management
Evaluation as the Delivery Language
- Creating golden test sets from real-world work
- Implementing automated scoring and LLM-based judges
- Establishing release gates linked to evaluation thresholds
Guardrails, Safety and Human Oversight
- Applying guardrails at the prompt, tool, and policy levels
- Integrating escalation paths and human review checkpoints
- Maintaining audit trails and fulfilling data handling obligations
Deployment Architecture in the Customer Estate
- Addressing identity, network, and data residency constraints
- Implementing model gateways and multi-provider abstraction layers
- Using infrastructure as code and standardised release processes
Observability, Cost and Token Economics
- Tracing agent executions and tool interactions
- Creating dashboards to monitor quality and expenditure
- Reducing costs while maintaining output quality
Adoption and Change Management
- Enabling the staff members performing the work
- Establishing champion networks and continuous feedback loops
- Measuring the velocity of user adoption
From Engagement to Reusable Capability
- Packaging proven workflows for reuse
- Developing playbooks, blueprints, and acceleration kits
- Facilitating smooth handovers to internal teams
Requirements
- Proficiency in Python, including experience with APIs and virtual environments
- Familiarity with cloud platforms, containers, and version control systems
- Prior experience calling LLM APIs or building prompt-based features
- Basic understanding of enterprise systems such as CRM, ERP, or ticketing platforms
Audience
- Forward deployed and applied AI engineers
- Software engineers transitioning into customer-facing AI delivery
- Technical consultants and solutions architects managing AI rollouts
- Platform engineers supporting production AI workloads
