Forward Deployed Engineer (Artificial Intelligence)

London, United Kingdom Posted on 10/11/2026

"Deloitte sets therequirements for the assignment. Rockford Resourcing acts as the agency onbehalf of Deloitte and is responsible for sourcing candidates to fulfil theassignment. Successful candidates will be employed by Rockford Payroll.Rockford Payroll is responsible for the Associate's employment, pay andapplicable employment-related processes."

The Role

This roleexists because high-impact AI delivery often requires engineers embedded withusers to rapidly discover the right AI approach, prove value beyond simplebaselines, and then harden and deliver AI capabilities into productionpathways. The AI Engineer accelerates learning loops while maintainingengineering discipline, ensuring what's built can scale, be supported and bereused.

Key Responsibilities

• Advanced modelling and algorithmselection - Choose and implement more complex approaches when needed (deeplearning, graph ML, NLP, GenAI, multimodal, optimisation, RL whereappropriate). Build prototypes that demonstrate lift over simpler baselines andjustify 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 androbust evaluation harnesses.
• Evaluation frameworks & AI quality- Create robust offline and online evaluation (golden datasets,human-in-the-loop review, red teaming, safety testing). Define modelconfidence, uncertainty handling and error taxonomies to drive measurablequality improvements.
• Model efficiency & productionreadiness - 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 defineguardrails and policies-as-code patterns with engineering and risk partners.
• Deployment partnership - Packagemodels, prompts and retrieval components with MLOps/engineering; define batchvs real-time serving patterns where relevant. Specify monitoring (quality,drift, safety signals, cost/latency) and operational thresholds; supportproduction handover.
• Reusable components - Build sharedlibraries, templates, evaluation harnesses and patterns that multiple squadscan adopt. Prefer reuse over bespoke and contribute reference implementationsback to communities of practice.
• Technical leadership - Coach the squadon best practices; align with central AI standards; review architecture choicesand implementation quality. Keep delivery moving with crisp weekly milestonesand transparent trade-offs.

Experience& Skills Required

• Proven experience delivering AI/MLsolutions into production pathways, including reproducible training/inferenceand clear handover artefacts.
• Strong ability to rapidly prototype,iterate with users, and then harden AI solutions towards production readiness.
• Experience selecting and implementingadvanced modelling approaches (e.g., deep learning/NLP/graph/GenAI) anddemonstrating lift over baselines.
• Experience designing robust evaluationframeworks (offline/online), including golden datasets, human-in-the-loopreview and clear quality metrics.
• Working knowledge of AI safety andsecurity risks (e.g., prompt injection, data leakage, privacy constraints) andhow to implement practical technical mitigations.
• Experience collaborating with productand business stakeholders to shape MVP scope, define acceptance criteria, andmanage trade-offs.
• Comfortable operating in ambiguousenvironments with shifting requirements, while maintaining delivery disciplineand transparency.