- Bangalore, Karnataka, India +1 more
- Staff
- 8+ years
Lead the next evolution of the AI assistant platform by defining and building highly scalable Generative AI systems and infrastructure.
deloitte · Posted Nov 10, 2026
Role: Forward Deployed Engineer Location: London/Sheffield Start Date: ASAP End Date: 6 months Daily Rate: Inside IR35 Payroll provider – Rockford Payroll Info for Contingent Workers – Rockford Pay “Deloitte sets the requirements for the assignment. Rockford Resourcing acts as the agency on behalf of Deloitte and is responsible for sourcing candidates to fulfil the assignment. Successful candidates will be employed by Rockford Payroll. Rockford Payroll is responsible for the Associate’s employment, pay and applicable employment-related processes.” The Role 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. Key Responsibilities · 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. Experience & Skills Required · Proven experience delivering AI/ML solutions into production pathways, including reproducible training/inference and clear handover artefacts. · Strong ability to rapidly prototype, iterate with users, and then harden AI solutions towards production readiness. · Experience selecting and implementing advanced modelling approaches (e.g., deep learning/NLP/graph/GenAI) and demonstrating lift over baselines. · Experience designing robust evaluation frameworks (offline/online), including golden datasets, human-in-the-loop review and clear quality metrics. · Working knowledge of AI safety and security risks (e.g., prompt injection, data leakage, privacy constraints) and how to implement practical technical mitigations. · Experience collaborating with product and business stakeholders to shape MVP scope, define acceptance criteria, and manage trade-offs. · Comfortable operating in ambiguous environments with shifting requirements, while maintaining delivery discipline and transparency.