9 of 9 Remote/Hybrid MLOps Jobs in Oxfordshire

technical stakeholders. Desirable Experience The following would be advantageous: Multimodal AI and reasoning. Edge or offline AI deployments. Kubernetes, particularly EKS or OpenShift. MLOps, including model evaluation, monitoring and reproducibility. Observability for agentic AI systems, including model performance, agent behaviour and drift. Agent orchestration and inter-agent communication protocols such ...

writing clean, efficient, and modular code suitable for production environments. Desirable Skills or Knowledge Familiarity with AMO physics or quantum machine learning. Experience with MLOps best practices. Knowledge of systems like Apache Kafka, MQTT or real‐time data pipelines. Experience with cloud platforms (AWS, Azure, GCP). Deep expertise ...

enterprise and industry-standard software platforms. A strong understanding of infrastructure security, reliability and operational best practice. Desirable Experience The following would be advantageous: MLOps or LLMOps experience. Experience with platforms such as SageMaker, Kubeflow or ZenML . Extensive on-premises Kubernetes deployment experience. Prometheus or comparable observability platforms. ...

Preferred Experience with multimodal reasoning Experience with edge or offline AI deployments Familiarity with Kubernetes (EKS/OpenShift) for monitoring and managing deployed applications MLOps experience - model evaluation, monitoring, reproducibility Observability tooling for agentic systems (model drift, agent behaviour, performance monitoring) Experience with agent orchestration patterns and inter‐agent communication ...

Senior MLOps & Data EngineerLocation: Oxford Based (Hybrid)Type: PermanentSalary: 90,000 - 120,000 + Bonus + Equity + BenefitsThe Opportunity We're supporting an innovative biotechnology organisation that is investing heavily in the next generation of data, machine learning and scientific computing capabilities.As part of a growing technology team … workflow automation and machine learning operations. You will help transform research and analytical workflows into reliable, secure and scalable production systems.ResponsibilitiesDesign and build scalable MLOps infrastructure to support model deployment, monitoring, retraining and lifecycle management.Productionise machine learning and scientific computing workflows using Python, container technologies and modern software engineering practices.Develop ...

infrastructure Desirable attributes: Hands‐on experience building LLM‐based systems Experience testing non‐deterministic systems Understanding of statistics and inferential methods LangGraph, RAG, MCP, MLOps, IaaC, AWS What we offer Private Medical Insurance Dental Insurance Parental Support Salary‐Exchange Pension Employee Assistance Programme (EAP) Local Oxford Discounts Cycle‐to‐work ...

CODAS project and related initiatives at AND-E Lab. Ideal candidates have 5+ years in ML/AI, strong Python, cloud/MLOps experience, and a collaborative mindset to work with partners and stakeholders across experiments and #J-18808-Ljbffr ...

every project. Strong communication skills: you'll need to challenge stakeholder expectations sometimes, not just relay them. Bonus points: experience establishing ML engineering/MLOps best practice from scratch, background in regulated or enterprise environments, or a track record of driving reuse through shared frameworks and tooling. Why join ...

reliable, well-monitored, cost-effective models in production - deployment, retraining, monitoring and governance - and the service levels (SLAs) of the ML organization. Partnering with MLOps infrastructure team.Partner across the company. Translate commercial strategy into an ML roadmap with Product, Engineering, Design, and Analytics leadership; represent ML in executive planning … partners across Product and Engineering and shaping strategy, not just execution.A strategic player-coach's technical depth: current enough in modern ML, experimentation, and MLOps to set direction, review architecture, and hold the scientific bar.Strong grounding in experimentation and statistics — A/B testing, causal inference, and rigorous measurement ...