Generative AI Engineer

Purpose

Enable Architecture to operate effectively in a GenAI-driven environment by:

• Rapidly translating emerging GenAI usage, risks, and architectural patterns into internal governance tooling
• Embedding governance intent into automation, workflows, guardrails, and defaults, rather than relying solely on static reviews and documents
• Supporting adaptive governance, where the level of rigor varies by maturity, risk, novelty, and blast radius
• Allowing architecture governance to evolve continuously as GenAI capabilities, vendors, and patterns change
• This is applied engineering in service of architecture governance, not product development.
• Operating Model Forward Deployed (Internal Architecture Context)

Must Have

Baseline Engineering Capability:

• Applied Internal Tool Development
• Python or Node.js (TypeScript)
• Build small services, scripts, CLIs, and internal utilities
• Comfortable with REST APIs, JSON, async workflows
• Practical fullstack literacy
• Can connect backend logic to lightweight UIs, notebooks, portals, or CLIs
• Systems and data integration mindset
• Pull data from APIs, documents, tickets, repositories, and architecture artifacts
• Combine structured, semi-structured, and unstructured inputs
• Strong engineering hygiene
• Readable, maintainable code
• Tests, logging, traceability
• Secure handling of credentials and sensitive internal data

GenAI & AI-Assisted Development

Expected:

• Heavy, responsible use of AI coding assistants
• Code generation, refactoring, test creation, documentation
• Strong human-in-the-loop discipline
• Reviews and validates AI-generated output
• Uses AI to accelerate thinking and execution, not replace judgment

Pragmatic use of LLMs for governance enablement

• Classification, summarization, comparison, extraction
• Surfacing governance signals and inconsistencies
• Assisting with evidencing and analysis, not making autonomous decisions

Architecture, Governance & Risk Awareness

Elevated:

• Architecture Governance Literacy
• Solid understanding of: Architecture principles, standards, patterns, and guardrails
• Design reviews, ADRs, ICAG-style governance forums
• Re-use before buy before build

Understands:

The intent behind governance controls:

• Where flexibility is appropriate
• Where evidencing and rigor are non-negotiable
• Adaptive Governance & Risk Thinking

Awareness of multiple risk dimensions relevant to GenAI:

• Model and data risk
• Security and privacy
• Operational resiliency
• Vendor dependency and lock-in
• Regulatory and audit sensitivity