351 to 375 of 3,223 Machine Learning Jobs in the UK

Learning Engineer to join our team and build the retrieval and knowledge representation systems at the heart of the platform. You will own ML components end to end — from research and prototyping through to production deployment — working across knowledge bases, vector stores, RAG pipelines, and context engines to power … structured reasoning over enterprise data. Design and implement RAG pipelines, including chunking, embedding, indexing, retrieval, and reranking. Build and maintain integrations between retrieval and ML components and diverse enterprise data sources, vector databases, APIs, and services. Develop context retrieval systems that balance recall, precision, latency, and cost. Build evaluation frameworks ...

Kubernetes, cloud platforms (GCP preferred), CI/CD, Git, DevOps tooling, automation frameworks, and Linux administration.* Cyber Security, SIEM/SOAR, on-premises infrastructure, Machine Learning/AI, and SAFe Agile exposure.* Strong analytical and problem-solving skills with attention to detail.* Good communication and collaboration skills with ...

legacy ways of working where appropriate. Capability & Leadership Support Mentoring: Support and mentor engineers within CreateFuture and our client teams, helping them navigate their learning and technical skill gaps. Community Contribution: Actively engage in the internal engineering communities, helping to build out an internal repository of reference architectures, Infrastructure … Azure AZ-104). At least one professional-level certification. Progressing toward or holding multiple professional certifications such as CKA (Certified Kubernetes Administrator), AWS ML Engineer Associate, or FinOps Certified Practitioner. What we’ll offer you: We trust people to do their best work. That means flexibility over rigid rules ...

legacy ways of working where appropriate. Capability & Leadership Support Mentoring: Support and mentor engineers within CreateFuture and our client teams, helping them navigate their learning and technical skill gaps. Community Contribution: Actively engage in the internal engineering communities, helping to build out an internal repository of reference architectures, Infrastructure … Azure AZ-104). At least one professional-level certification. Progressing toward or holding multiple professional certifications such as CKA (Certified Kubernetes Administrator), AWS ML Engineer Associate, or FinOps Certified Practitioner. What we’ll offer you: We trust people to do their best work. That means flexibility over rigid rules ...

legacy ways of working where appropriate. Capability & Leadership Support Mentoring: Support and mentor engineers within CreateFuture and our client teams, helping them navigate their learning and technical skill gaps. Community Contribution: Actively engage in the internal engineering communities, helping to build out an internal repository of reference architectures, Infrastructure … Azure AZ-104). At least one professional-level certification. Progressing toward or holding multiple professional certifications such as CKA (Certified Kubernetes Administrator), AWS ML Engineer Associate, or FinOps Certified Practitioner. What we’ll offer you: We trust people to do their best work. That means flexibility over rigid rules ...

legacy ways of working where appropriate. Capability & Leadership Support Mentoring: Support and mentor engineers within CreateFuture and our client teams, helping them navigate their learning and technical skill gaps. Community Contribution: Actively engage in the internal engineering communities, helping to build out an internal repository of reference architectures, Infrastructure … Azure AZ-104). At least one professional-level certification. Progressing toward or holding multiple professional certifications such as CKA (Certified Kubernetes Administrator), AWS ML Engineer Associate, or FinOps Certified Practitioner. What we’ll offer you: We trust people to do their best work. That means flexibility over rigid rules ...

equivalent practical experience. 3 years of experience testing, maintaining, or launching software products. 3 years of experience with software design and architecture. Experience in Machine Learning. Preferred qualifications: Master's degree or PhD in Computer Science or related technical field. 5 years of experience with data structures and algorithms. … problems across the full-stack as we continue to push technology forward. Our team has a decade-long track record of successfully bringing ML ideas from theory to practice in the world of online ads. We have made it much easier for small and medium‐sized content creators to grow ...

industrial process data, serving sectors like pharmaceuticals, energy, and manufacturing. Our core product is a robust calculation engine capable of executing advanced math and machine learning algorithms on streaming time-series data. By leveraging generative AI, we enhance usability and accelerate outcomes for our users. As a fully ...

Preferred Qualifications Experience with data analytics tools (e.g., SQL, Tableau, Python) to derive insights and support decision-making. Familiarity with emerging technologies like AI, machine learning, and DLT within financial services. Prior experience working within a financial services technology platform or a similar business unit. ...

digital workflow gained through training, certification or equivalent experience. Understanding of AI and automation technologies such as RPA, workflow automation, scripting, APIs, NLP, machine learning or low-code platforms and their safe use in healthcare. Knowledge of information governance, data protection, cyber security, clinical safety and testing principles ...

Data platforms, tooling, and cloud infrastructure. A collaborative environment working alongside Product, Architecture, Data Science, and Engineering leadership. Opportunities to attend external AI/ML events and conferences. Technical development resources, including access to relevant learning platforms, certifications, and emerging technology previews. Benefits Generous colleague discount across Sainsbury ...

where every voice matters. The Position The Engineering - AI Enablement group, within Roche's Computational Sciences Center of Excellence, exists to make AI/ML an everyday utility for the scientists driving drug discovery. As a Senior Machine Learning Engineer, you will build, own, and continuously improve … scalable AI/ML systems, applications, and data infrastructure that unlock the potential of our diverse scientific data, accelerating the discovery and development of life-changing treatments. You will work across a cross-functional ecosystem of computational and research scientists, AI/ML experts, product leaders, and DevOps engineers, spanning ...

lead development and deployment of cutting‐edge AI systems for our diverse clients. You’ll design, build, and deploy scalable, production‐grade ML software and infrastructure that meets rigorous operational and ethical standards. This is an ambitious, cross‐functional role requiring a blend of technical expertise, engineering leadership, and confident … client‐facing skills. Responsibilities Lead technical scoping and architectural decisions for high‐impact ML systems. Design and build production‐grade ML software, tools, and scalable infrastructure. Define and implement best practices and standards for deploying machine learning at scale across the business. Collaborate with engineers, data scientists, product ...

financial environments. Hybrid working, based in Scotland. What you'll do Set the AI and GenAI architecture strategy and roadmap. Build scalable AI/ML and Agentic AI platforms spanning LLMs, retrieval-augmented generation (RAG), embeddings and semantic search. Lead on deployment, governance and integration patterns, advise clients on platform … agent frameworks, vector databases, MLOps/LLMOps, AWS, Azure, GCP, Databricks, Kubernetes and GPU infrastructure. What we're looking for Proven enterprise AI or ML architecture design, strong cloud and modern data platform knowledge, and demonstrable experience of scalable AI deployment and integration. Financial services or another regulated industry ...

business outcomes. Key responsibilities: Translating business objectives into AI architecture strategies, roadmaps and scalable solution designs Designing and implementing end to end AI and ML architectures across enterprise environments Defining scalable, performant and cost optimised deployment patterns across cloud, containerised and GPU enabled environments Supporting AI implementation programmes from proof … transformation programmes Producing solution design documentation, implementation plans and technical proposals Providing technical leadership and mentoring within multidisciplinary teams Experience Required: Experience designing AI, ML or modern data architectures within enterprise environments Strong understanding of cloud platforms such as AWS, Azure, GCP, Databricks or similar technologies Experience architecting scalable ...

business outcomes. Key responsibilities: Translating business objectives into AI architecture strategies, roadmaps and scalable solution designs Designing and implementing end to end AI and ML architectures across enterprise environments Defining scalable, performant and cost optimised deployment patterns across cloud, containerised and GPU enabled environments Supporting AI implementation programmes from proof … transformation programmes Producing solution design documentation, implementation plans and technical proposals Providing technical leadership and mentoring within multidisciplinary teams Experience Required: Experience designing AI, ML or modern data architectures within enterprise environments Strong understanding of cloud platforms such as AWS, Azure, GCP, Databricks or similar technologies Experience architecting scalable ...

business outcomes. Key responsibilities: Translating business objectives into AI architecture strategies, roadmaps and scalable solution designs Designing and implementing end to end AI and ML architectures across enterprise environments Defining scalable, performant and cost optimised deployment patterns across cloud, containerised and GPU enabled environments Supporting AI implementation programmes from proof … transformation programmes Producing solution design documentation, implementation plans and technical proposals Providing technical leadership and mentoring within multidisciplinary teams Experience Required: Experience designing AI, ML or modern data architectures within enterprise environments Strong understanding of cloud platforms such as AWS, Azure, GCP, Databricks or similar technologies Experience architecting scalable ...

Near Real-Time and Batch Inferencing: Use infrastructure like Spark and Ray to stand up inferencing services that integrate with operational/analytics workloads. ML Infrastructure: Help build a first-class machine learning platform from the ground up which helps manage the entire model lifecycle: feature engineering, model … experience in applied machine learning on real recommendations use cases (brownie points for productionised sequential learning use cases!). Experience with ML/distributed ML frameworks like Ray, Spark-MLlib, TensorFlow etc. Experience with real-time scoring/evaluation of models with low latency constraints. Great coding ...

Production Solution Delivery: Lead impactful customer technical projects by delivering production‐grade systems, designing and building reference architectures, custom applications and data ingestion and ML/AI model integration Transformational Impact: Guide strategic customers as they implement transformational big data projects including end-to-end design, build and deployment … with distributed computing with Apache SparkTM and knowledge of Spark runtime internals Familiarity with CI/CD for production deployments Working knowledge of MLOps, ML/AI models and AI APIs Design and deployment of performant production end-to-end data architectures and applications that combine data pipelines, ML/ ...

Machine Learning Engineer, you will play a central role in making those systems production-ready. That means owning the engineering that takes ML models and AI capabilities from experimentation into reliable, scalable production use: the pipelines, the deployment tooling, the monitoring, and the shared components that make … quality of delivery across a fast-moving and expanding set of AI capabilities and systems. Some of the great things you'll be doing: ML Engineering and AI Systems Own the end-to-end delivery of production machine learning and AI solutions in collaboration with data scientists ...

technologies (RDF, SPARQL, LPG, SHACL) Experience in schema design, ontology management, and KG curation Expertise in designing and developing KG solutions and graph-based ML models (functional + technical) Experience with end‐to‐end data pipeline implementation for AI applications (esp. LLMs), with hands‐on design and configuration Experience with … ability to work across engineering, research, and product teams across multiple time zones. You have external client‐facing consulting experience Broad experience in diverse ML techniques and agentic systems Locations London Equal Employment Opportunity Statement All employment decisions shall be made without regard to age, race, creed, color, religion ...

healing automation for known failure patterns, reducing human intervention and on-call burden by 50%+ Build comprehensive observability stacks (metrics, logs, traces) with ML-driven anomaly detection and AIOps capabilities Lead automated incident management: detection, triage, escalation, remediation, and post-incident review automation Drive DB automation: automated provisioning, release … deployment pipelines TECHNICAL SKILLS & EXPERTISE Expert-level observability: Prometheus, Grafana, ELK/OpenSearch, Jaeger/Zipkin, Datadog, or Dynatrace Strong experience with AIOps and ML-driven monitoring: PagerDuty, Moogsoft, BigPanda, or custom ML pipelines Deep knowledge of FMEA, fault tree analysis, and chaos engineering tools (Gremlin, LitmusChaos, Chaos Monkey) Database ...

systems including power, thermal, and IT infrastructure R&D & Innovation Lead development of next‐generation capabilities including: Digital twin integration and simulation AI/ML‐driven analytics and optimization Advanced automation and control strategies Collaborate with strategic partners and technology providers to accelerate innovation Translate emerging technologies into practical, scalable … manner Additional Preferred Experience Experience with platform‐based product development and standardisation across multiple business units Experience with digital twins, simulation, or AI/ML applications in industrial systems Experience working with cloud platforms and hybrid architectures Experience collaborating with external technology partners and ecosystems PHYSICAL & ENVIRONMENTAL DEMANDS No special ...

tech lead or staff engineer at a SaaS or enterprise software vendor with a forward‐deployed or professional services motion. Experience with AI/ML platform deployments or data‐heavy enterprise applications at scale. Familiarity with major cloud environments: AWS, Azure, or GCP, including managed ML/AI services. Experience ...

SHACL). Advanced practical knowledge of schema design, ontology management, and KG curation. Seasoned in designing and developing KG solutions and graph-based ML models (functional + technical). Proven experience in end-to-end data pipeline implementations for AI applications (esp. LLMs), with hands‐on design and configuration. Extensive … have external client‐facing consulting experience. Ph.D. in Computer Science, Computer or Electrical Engineering, Mathematics, or a related field. Broad experience in diverse ML techniques and agentic systems. Locations London Berlin Madrid Paris Equal Employment Opportunity Statement All employment decisions shall be made without regard to age, race, creed, color ...