At a Glance
- Tasks: Build and maintain scalable ML systems, leading Kubernetes orchestration and CI/CD automation.
- Company: Join a forward-thinking Data & AI Engineering team shaping the future of AI.
- Benefits: Generous pension, annual bonus, share schemes, 30 days holiday, and wellbeing initiatives.
- Other info: Exciting growth opportunities in a dynamic environment focused on fairness and accountability.
- Why this job: Make a real impact in AI innovation while working with cutting-edge technologies.
- Qualifications: Strong Python skills and expertise in Kubernetes, CI/CD, and GPU optimisation required.
The predicted salary is between 60000 - 80000 £ per year.
Exciting opportunity for a hands-on ML/AI Engineer to join our Data & AI Engineering team. You’ll build, automate, and maintain scalable systems that support the full machine learning lifecycle. You will lead Kubernetes orchestration, CI/CD automation (including Harness), GPU optimisation, and large-scale model deployment, owning the path from code commit to reliable, monitored production services. This is a unique opportunity to shape the future of AI by embedding fairness, transparency, and accountability at the heart of innovation. You’ll join us at an exciting time as we move into the next phase of our transformation. We’re looking for curious, passionate engineers who thrive on innovation and want to make a real impact.
Responsibilities
- Compose, build, and operate production-grade Kubernetes clusters for high-volume model inference and scheduled training jobs.
- Configure autoscaling, resource quotas, GPU/CPU node pools, service mesh, Helm charts, and custom operators to meet reliability and efficiency targets.
- Implement GitOps workflows for environment configuration and application releases.
- Build CI/CD pipelines in Harness (or equivalent) to automate build, test, model packaging, and deployment across environments (dev / pre-prod / prod).
- Enable progressive delivery (blue/green, canary) and rollback strategies, integrating quality gates, unit/integration tests, and model-evaluation checks.
- Standardise pipelines for continuous training (CT) and continuous monitoring (CM) to keep models fresh and safe in production.
- Deploy and tune GPU-backed inference services (e.g., A100), optimise CUDA environments, and leverage TensorRT where appropriate.
- Operate scalable serving frameworks (NVIDIA Triton, TorchServe) with attention to latency, efficiency, resilience, and cost.
- Implement end-to-end observability for models and pipelines: drift, data quality, fairness signals, latency, GPU utilisation, error budgets, and SLOs/SLIs via Prometheus, Grafana, and Dynatrace.
- Establish actionable alerting and runbooks for on-call operations; drive incident reviews and reliability improvements.
- Operate a model registry (e.g., MLflow) with experiment tracking, versioning, lineage, and environment-specific artefacts.
- Enforce audit readiness: model cards, reproducible builds, provenance, and controlled promotion between stages.
Qualifications
- Strong Python for automation, tooling, and service development.
- Deep expertise in Kubernetes, Docker, Helm, operators, node-pool management, and autoscaling.
- CI/CD expertise having hands-on experience with Harness (or similar) building multi-stage pipelines; experience with GitOps, artefact repositories, and environment promotion.
- Practical experience with CUDA, TensorRT, Triton, TorchServe, and GPU scheduling/optimisation.
- Proficiency in Prometheus, Grafana, Dynatrace defining SLIs/SLOs and alert thresholds for ML systems.
- Experience operating MLflow (or equivalent) for experiment tracking, model bundling, and deployments.
- Expert use of Git, branching models, protected merges, and code-review workflows.
It would be great if you had any of the following:
- Experience with GCP (e.g., GKE, Cloud Run, Pub/Sub, BigQuery) and Vertex AI (Endpoints, Pipelines, Model Monitoring, Feature Store).
- Hooks for prompt/version management, offline/online evaluation, and human-in-the-loop workflows (e.g., RLHF) to enable continuous improvement.
- Familiarity with Model Context Protocol (MCP) for tool interoperability, plus Google ADK, LangGraph/LangChain for agent orchestration and multi-agent patterns.
- Ray, Kubeflow, or similar frameworks.
- Experience embedding controls, audit evidence, and governance in regulated environments.
- Experience with GPU efficiency, autoscaling strategies, and workload right-sizing.
Benefits
- A generous pension contribution of up to 15%
- An annual bonus award, subject to Group performance
- Share schemes including free shares
- Benefits you can adapt to your lifestyle, such as discounted shopping
- 30 days holiday, with bank holidays on top
- A range of wellbeing initiatives and generous parental leave policies
ML/AI Engineer in Manchester employer: LLOYDS BANKING GROUP
Join our innovative Data & AI Engineering team as an ML/AI Engineer, where you'll have the opportunity to shape the future of AI while working in a collaborative and forward-thinking environment. We offer a generous benefits package, including a pension contribution of up to 15%, 30 days of holiday, and a range of wellbeing initiatives, all designed to support your personal and professional growth. With a focus on fairness, transparency, and accountability, you'll be part of a culture that values curiosity and passion, making a real impact in the world of machine learning.
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We think you need these skills to ace ML/AI Engineer in Manchester
Some tips for your application 🫡
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✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
