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Duration 28 hours
Course Outline
The AI Forward Deployed Engineer
- Distinguishing the role from ML engineers and solutions engineers
- Focusing on outcome ownership rather than delivery milestones
- Collaborating with product, research, and security teams
Discovery in Complex Enterprise Environments
- Observing workflows from start to finish
- Mapping systems, data flows, and handoff points
- Identifying the primary constraint hindering value creation
Problem Scoping and Prioritisation
- Evaluating use cases for technical feasibility
- Scoring potential workflows based on effort required
- Selecting the initial workflow for proof of concept
Value Framing and Acceptance Criteria
- Establishing metric baselines prior to development
- Articulating the business case for finance sponsors
- Agreeing on acceptance criteria 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
- Determining when data regulations necessitate locally hosted models
Context Engineering and Prompt Design
- Structuring instructions to ensure reliable behaviour
- Assembling context from systems of record
- Implementing prompt caching and token efficiency
Retrieval and Knowledge Grounding
- Chunking and indexing enterprise content
- Applying hybrid search, reranking, and citation methods
- Evaluating retrieval performance independently of generation
Agents and Tool Use
- Designing tools and typed interfaces
- Building with 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
- Aligning tool permissions with enterprise identity management
Evaluation as the Delivery Language
- Creating golden sets derived from real workloads
- Using automated scoring and LLM judges
- Defining release gates linked to evaluation thresholds
Guardrails, Safety, and Human Oversight
- Implementing guardrails at prompt, tool, and policy levels
- Establishing escalation and human review checkpoints
- Maintaining audit trails and fulfilling data handling responsibilities
Deployment Architecture in the Customer Estate
- Navigating identity, network, and data residency constraints
- Utilising model gateways and multi-provider abstractions
- Applying infrastructure as code and structured release processes
Observability, Cost, and Token Economics
- Tracing agent executions and tool interactions
- Monitoring quality and expenditure via dashboards
- Reducing costs without compromising output quality
Adoption and Change Management
- Enabling the personnel performing the core work
- Building champion networks and feedback loops
- Measuring adoption velocity and engagement
From Engagement to Reusable Capability
- Packaging validated workflows
- Developing playbooks, blueprints, and accelerators
- Facilitating handover to internal teams
Requirements
- Proficiency in Python, including API integration and virtual environment management
- Familiarity with cloud platforms, containerisation, and version control systems
- Previous experience interacting with LLM APIs or developing prompt-based features
- Fundamental 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
- Technical consultants and solutions architects overseeing AI implementations
- 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 5200 € + VAT*
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