ML/AI Engineer

ML/AI Engineer – Salary £70,929‑£85,000 per annum. Location: Manchester. Hours: Full‑time – 35 hours. Working pattern: Hybrid – at least two days per week at Manchester office.

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 dev / pre‑prod / prod environments.
• 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.
• 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 with hands‑on experience in 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.

Preferred Experience

• 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%
• 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

Equality, Diversity & Inclusion

We are committed to building an inclusive workplace and encourage applications from under‑represented groups. We are disability confident and welcome reasonable adjustments during recruitment.

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