AI ML Engineer - #2195007

Greetings

We are Hiring

Forward Deployed Engineer – AI / ML Engineer - FDT

Sheffield, UK

3 days onsite

This role bridges product intent, data science and engineering execution, accelerating time-to-value while ensuring AI solutions are secure, supportable, efficient and aligned to enterprise standards and platforms.

Reporting & stakeholders:

Solid line into FDT Tech Lead. Strong day-to-day partnership with Product Owner(s), business SMEs, data/ML colleagues and delivery pod leads on scope, prioritisation and trade-offs. Close collaboration with platform teams (e.g., AI platform / Data platform), architecture, and operations/support teams to ensure production readiness. Engage with tooling/platform owners to provide structured feedback and reusable patterns from field delivery.

Scope & authority: Accountable for shaping, building and delivering advanced AI prototypes and end-to-end thin-slice AI solutions close to users, including model selection, GenAI solution design, evaluation, safety controls, and efficiency optimisation. Owns local technical decisions required to deliver outcomes at pace, within approved architecture patterns, engineering standards, controls and governance frameworks. Authority includes recommending AI approaches and evaluation methods, implementing guardrails and policies-as-code patterns, and escalating material risks, dependencies or control gaps.

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.

Delivering AI solutions into production pathways with reproducibility and handover artefacts

Advanced modelling and algorithm selection with measurable lift over baselines

Evaluation frameworks (offline/online), golden datasets, and quality metrics

AI safety/security risk identification and technical mitigations (prompt injection, data leakage, privacy)

Rapid prototyping and iterative delivery with users