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 Role of the AI Forward Deployed Engineer
- Distinguishing the role from ML engineers and solutions engineers
- Focusing on business outcomes rather than delivery milestones
- Collaborating effectively with product, research, and security teams
Discovery in Complex Enterprise Environments
- Observing workflows from start to finish
- Mapping existing systems, data flows, and handoff points
- Identifying the primary constraints blocking value creation
Scoping and Prioritising Problems
- Evaluating use cases for technical feasibility
- Scoring candidate workflows based on required effort
- Selecting the initial workflow for proof of concept
Defining Value and Acceptance Criteria
- Establishing baseline metrics before development begins
- Formulating business cases for financial 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 window, latency, and unit costs
- Determining when data regulations require locally hosted models
Context Engineering and Prompt Design
- Structuring instructions for consistent and reliable behaviour
- Gathering context from authoritative systems of record
- Implementing prompt caching and token efficiency strategies
Retrieval and Knowledge Grounding
- Chunking and indexing corporate content
- Utilising hybrid search, reranking, and citation mechanisms
- Assessing retrieval quality independently from generation
Agents and Tool Utilization
- Designing tools with typed interfaces
- Developing using OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure states
MCP and Enterprise Integration
- Implementing the Model Context Protocol (2026-07-28 revision)
- Developing and securing MCP servers
- Aligning tool permissions with enterprise identity systems
Evaluation as the Core Delivery Metric
- Creating golden test sets from real-world tasks
- Applying automated scoring and LLM-based judges
- Setting release gates based on evaluation thresholds
Guardrails, Safety, and Human Oversight
- Implementing guardrails at prompt, tool, and policy levels
- Defining escalation paths and human review checkpoints
- Managing audit trails and data compliance duties
Deployment Architecture in Client Environments
- Navigating identity, network, and data residency requirements
- Using model gateways and multi-provider abstractions
- Applying infrastructure as code and structured release processes
Observability, Cost, and Token Economics
- Tracing agent executions and tool invocations
- Building dashboards for quality monitoring and spend analysis
- Reducing costs without compromising output quality
Adoption and Change Management
- Enabling the end-users performing the work
- Building champion networks and feedback loops
- Tracking adoption speed and impact
Transforming Engagements into Reusable Capabilities
- Packaging validated workflows
- Creating playbooks, blueprints, and accelerators
- Facilitating handover to internal teams
Requirements
- Strong Python skills, including API integration and virtual environment management
- Working knowledge of cloud platforms, containerisation, and version control
- Previous experience interacting with LLM APIs or developing prompt-based features
- Foundational understanding of enterprise systems like CRM, ERP, or ticketing platforms
Target Audience
- Forward deployed and applied AI engineers
- Software developers transitioning into customer-facing AI delivery
- Technical consultants and solutions architects overseeing AI implementations
- Platform engineers managing production AI workloads
