AI Forward Deployed Engineer: Az elkészült AI-rendszerek hatáskörének meghatározása, építése és bevezetése Képzés

The AI Forward Deployed Engineer

• The role versus ML engineer and solutions engineer
• Owning outcomes rather than delivery milestones
• Working with product, research and security teams

Discovery in Messy Enterprises

• Shadowing a workflow end to end
• Mapping systems, data and handoffs
• Finding the constraint that blocks value

Problem Scoping and Prioritisation

• Qualifying use cases for feasibility
• Scoring candidate workflows against effort
• Choosing the first workflow to prove

Value Framing and Acceptance Criteria

• Baselining metrics before any build
• Business case language for finance sponsors
• Agreeing acceptance criteria with users

Model Selection: Frontier and Open Weights

• Comparing GPT-6, Claude 5.5 and open models
• Context, latency and unit cost trade-offs
• When data rules force locally hosted models

Context Engineering and Prompt Design

• Structuring instructions for reliable behaviour
• Assembling context from systems of record
• Prompt caching and token minimisation

Retrieval and Knowledge Grounding

• Chunking and indexing enterprise content
• Hybrid search, reranking and citation
• Evaluating retrieval separately from generation

Agents and Tool Use

• Tool design and typed interfaces
• Building with the OpenAI and Claude agent SDKs
• Approvals, retries and failure handling

MCP and Enterprise Integration

• The Model Context Protocol 2026-07-28 revision
• Building and securing MCP servers
• Permissioning tools against enterprise identity

Evaluation as the Delivery Language

• Golden sets built from real work
• Automated scoring and LLM judges
• Release gates tied to eval thresholds

Guardrails, Safety and Human Oversight

• Guardrails at prompt, tool and policy layers
• Escalation and human review points
• Audit trails and data handling duties

Deployment Architecture in the Customer Estate

• Identity, network and data residency constraints
• Model gateway and multi-provider abstraction
• Infrastructure as code and release process

Observability, Cost and Token Economics

• Tracing agent runs and tool calls
• Dashboards for quality and spend
• Cutting cost without losing quality

Adoption and Change

• Enablement for the people doing the work
• Champion networks and feedback loops
• Measuring adoption velocity

From Engagement to Reusable Capability

• Packaging proven workflows
• Playbooks, blueprints and accelerators
• Handing over to internal teams