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.