Senior AI Engineer in Bury

The Senior AI Engineer is a senior individual contributor responsible for architecting, building and scaling production‑grade AI platforms and generative AI systems across JD Group. Reporting into the Head of Data Science & AI, the role focuses on the engineering, operationalisation and governance of large‑scale AI solutions, including LLM‑based applications, agentic workflows and retrieval‑augmented generation systems. Working closely with Senior Data Scientists, Data Engineering, Platform and Product teams, the Senior AI Engineer ensures AI solutions are reliable, secure, cost‑effective and embedded into core business processes. This role carries significant technical leadership, mentorship and influence across the wider Data & AI community.

Responsibilities:

• Architect, develop and deploy enterprise‑scale AI and GenAI solutions including LLM applications, agentic workflows and tool‑using agents.
• Design, implement and optimise production‑grade RAG architectures with strong performance, scalability and latency characteristics.
• Build AI services, microservices, inference pipelines and platform components using modern engineering frameworks and patterns.
• Own technical decisions across AI system design, orchestration, routing, caching and runtime optimisation.

Production Readiness, LLMOps & MLOps:

• Define and implement standards for LLMOps, MLOps, monitoring, observability, safety and compliance.
• Ensure AI systems are robust, monitored, explainable and suitable for long‑term production use.
• Partner closely with Platform, DevOps and Security teams to deliver cloud‑native, secure and scalable solutions on GCP.
• Drive cost‑efficient AI deployment strategies including prompt optimisation, model selection, caching, distillation and compute optimisation.

Governance, Risk & Responsible AI:

• Embed responsible AI principles into system design, including safety, security, bias mitigation and data protection.
• Support governance frameworks for model usage, evaluation, auditability and risk management.
• Develop automated evaluation, testing and quality assurance frameworks for LLM‑based systems.

Stakeholder Partnership & Influence:

• Work closely with Senior Data Scientists to productionise AI‑driven analytical and decisioning solutions.
• Partner with Product, Engineering and Architecture leaders to shape AI solution design and delivery.
• Contribute to strategic decisions on AI infrastructure, architecture and long‑term platform roadmap.
• Evaluate and onboard AI vendors and third‑party platforms, prioritising buy‑first solutions where appropriate.

Capability Building & Mentorship:

• Provide technical mentorship and guidance to AI Engineers and adjacent engineering teams.
• Contribute to shared platforms, reusable components, reference architectures and best practices.
• Stay current with advances in generative AI, agentic systems and AI infrastructure, identifying pragmatic opportunities to apply new capabilities.

Role Objectives & KPIs:

• Deliver production‑grade AI platforms and systems that generate measurable business value.
• Ensure AI solutions are scalable, reliable, secure and cost‑effective.
• Reduce operational risk through strong governance, automation and engineering standards.
• Successful end‑to‑end delivery of complex AI initiatives to agreed quality and timelines.
• Strengthen trust in AI as a decision‑making and operational capability.
• Strong stakeholder satisfaction and trust in AI delivery.
• Act as a senior technical role model within the Data Science & AI function.

Skills and Experience:

• Significant experience in AI Engineering, ML Engineering or Software Engineering with proven production delivery.
• Deep expertise in LLMs, generative AI, agentic systems, RAG architectures and vector databases.
• Strong experience building distributed systems, microservices and scalable API‑driven platforms.
• Advanced experience with GCP AI stack (Vertex AI, BigQuery, Cloud Run, Cloud Functions, Cloud SQL, Agent Engine, AlloyDB etc.).
• Strong Python skills and experience building production‑grade AI services.
• Experience implementing LLMOps, MLOps, CI/CD and infrastructure automation.
• Expertise in developing applications with React, NextJS.
• Strong understanding of responsible AI, security, governance and data compliance.
• Ability to influence technical direction and communicate effectively with senior stakeholders.
• Experience in large‑scale, multi‑brand, or global enterprises; retail experience is advantageous.
• Delivery‑focused, pragmatic, and accountable.
• Line management/mentoring experience will be preferable.