Role Description & Core Accountabilities

This role exists because high-impact AI delivery often requires engineers embedded with users to rapidly discover the right AI approach, prove value beyond simple baselines, and then harden and deliver AI capabilities into production pathways. The AI Engineer accelerates learning loops while maintaining engineering discipline, ensuring what’s built can scale, be supported and be reused.

• Advanced modelling and algorithm selection: Choose and implement more complex approaches when needed (deep learning, graph ML, NLP, GenAI, multimodal, optimisation, RL where appropriate). Build prototypes that demonstrate lift over simpler baselines and justify added complexity.
• LLM/GenAI solution design (if in scope): Define prompting strategy, tool/function calling, RAG design (chunking, embeddings, retrieval evaluation), context window management and guardrails. Manage hallucination risk through grounding, citations, fallback behaviours and robust evaluation harnesses.
• Evaluation frameworks & AI quality: Create robust offline and online evaluation (golden datasets, human-in-the-loop review, red teaming, safety testing). Define model confidence, uncertainty handling and error taxonomies to drive measurable quality improvements.
• Model efficiency & production readiness: Optimise for latency and cost (distillation, quantisation, caching, batching, model selection). Ensure reproducibility and handover-ready artefacts (model cards, evaluation reports, reproducible pipelines).
• AI safety, security, and controls (technical depth): Address prompt injection and data exfiltration risks, privacy constraints, and secure use of embeddings/vector stores. Help define guardrails and policies-as-code patterns with engineering and risk partners.
• Deployment partnership: Package models, prompts and retrieval components with MLOps/engineering; define batch vs real-time serving patterns where relevant. Specify monitoring (quality, drift, safety signals, cost/latency) and operational thresholds; support production handover.
• Reusable components: Build shared libraries, templates, evaluation harnesses and patterns that multiple squads can adopt. Prefer reuse over bespoke and contribute reference implementations back to communities of practice.
• Technical leadership: Coach the squad on best practices; align with central AI standards; review architecture choices and implementation quality. Keep delivery moving with crisp weekly milestones and transparent trade-offs.