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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer
- Distinguishing this role from ML engineers and solutions engineers
- Focusing on outcomes rather than just delivery milestones
- Collaborating with product, research, and security teams
Discovery in Complex Enterprises
- Tracking a workflow from start to finish
- Mapping systems, data flows, and handoffs
- Identifying the specific constraint that hinders value delivery
Problem Scoping and Prioritisation
- Assessing use cases for feasibility
- Scoring potential workflows based on effort and impact
- Selecting the initial workflow to validate
Value Framing and Acceptance Criteria
- Establishing metric baselines before development begins
- Communicating the business case effectively to 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
- Aggregating context from various systems of record
- Implementing prompt caching and token optimisation
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content for retrieval
- Utilising hybrid search, reranking, and citation techniques
- Evaluating retrieval performance independently of generation quality
Agents and Tool Use
- Designing tools with typed interfaces
- Development using OpenAI and Claude agent SDKs
- Managing approvals, retries, and failure scenarios
MCP and Enterprise Integration
- Implementing the Model Context Protocol 2026-07-28 revision
- Building and securing MCP servers
- Aligning tool permissions with enterprise identity systems
Evaluation as the Delivery Language
- Creating golden datasets from real-world work scenarios
- Utilising automated scoring and LLM-based judging
- Tying release gates to specific evaluation thresholds
Guardrails, Safety and Human Oversight
- Implementing guardrails at the prompt, tool, and policy levels
- Establishing escalation paths and human review checkpoints
- Maintaining audit trails and adhering to data handling obligations
Deployment Architecture in the Customer Estate
- Adhering to identity, network, and data residency constraints
- Implementing model gateways and multi-provider abstraction
- Managing infrastructure as code and release processes
Observability, Cost and Token Economics
- Tracing agent executions and tool interactions
- Creating dashboards for quality monitoring and spend analysis
- Optimising costs without compromising system quality
Adoption and Change Management
- Empowering the personnel responsible for daily operations
- Establishing champion networks and feedback mechanisms
- Measuring the speed and effectiveness of adoption
From Engagement to Reusable Capability
- Packaging proven workflows for reuse
- Developing playbooks, blueprints, and accelerators
- Transferring knowledge to internal client teams
Requirements
- Proficiency in Python, including API integration and virtual environment management
- Familiarity with cloud platforms, containerisation, and version control systems
- Previous experience with LLM APIs or building prompt-based features
- Basic knowledge of enterprise platforms such as CRM, ERP, or ticketing systems
Target Audience
- Forward deployed and applied AI engineers
- Software engineers transitioning into client-facing AI delivery
- Technical consultants and solutions architects leading AI rollouts
- Platform engineers supporting production AI workloads
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 6400 € + VAT*
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