Artificial Intelligence Engineer

On behalf of a well-funded and rapidly growing early stage AI development company focused on the compliance space we are seeking an AI engineer who is excited by rapid experimentation and deep platform R\&D.

Role:

• Turn customer conversations into focused, working POCs on a short turnaround, then identify what should become a reusable platform capability
• Design and ship multi-agent pipelines for document generation, compliance checking, and BQ (Bill of Quantities) verification
• Build the intelligence layer that brings deep domain and compliance knowledge into every workflow, rather than solving the same problem customer by customer
• Benchmark frontier models (GPT, Claude, Gemini) against domain-specific tasks; own the evaluation framework
• Build retrieval, reasoning, and structured output layers — our documents need to be auditable, not just plausible
• Develop and maintain our internal skill library on the Mink platform — reusable AI agents deployable across client workflows
• Integrate CAD/drawing inputs with document output pipelines (cross-referencing drawings against BQ items, safety plans, method statements)
• Run bi-weekly research reviews — we stay at the frontier and implement what matters
• Work with domain experts (QS, safety engineers, civil engineers) to encode deep compliance logic into AI workflows

Requirements:

• 2\+ years hands-on AI/ML engineering \& 10\+ years in software development.
• Strong in LLMs, RAG, multi-agent systems, and structured output design.
• Python-first, hands-on with LangGraph (or a comparable agent-orchestration framework) — you can build, debug, and extend a real graph.
• Agent instincts — you've built or extended an orchestration loop (tools, state, retries, human-in-the-loop), not just called a chat API.
• You experiment before you theorise — new models, new frameworks, you test them and report what's real.
• Startup instincts — you operate in ambiguity and ship without a perfect spec.
• Document understanding, OCR pipelines, or structured compliance logic — a real plus.
• Comfortable across the stack — happy in a Python and a TypeScript web app in the same week.