776 to 800 of 4,924 Machine Learning Jobs in England

high-pressure production incidentsDesired* Experience with Site Reliability Engineering (SRE) practices, including monitoring, incident response, and post-mortem analysis* Proven experience applying AI or machine-learning models to optimise workflows, identify patterns, and drive intelligent automation solutions* Hands-on experience with containerization technologies such as Docker and Kubernetes ...

frameworks (e.g., EU AI Act). Global Leadership & Stakeholder Management Distributed Team Orchestration: Direct and empower distributed cross-functional teams (Software Engineers, AI/ML Engineers, Prompt Engineers, DevOps/LLMOps Specialists, QA Engineers) of 100+ people across global delivery centres. CTO & Engineering Leadership Advisory: Serve as the primary delivery … executives. Translate complex AI capabilities and metrics (cycle time, lead time, deployment frequency, model accuracy) into clear business outcomes. Engineering Culture & Upskilling: Drive continuous learning across delivery teams, championing modern software engineering standards, AI-driven development patterns, and prompt engineering best practices. What we offer Our goal ...

Fabric platform architecture and governance. Cloud data platform design and strategic architecture. AI Agents, Microsoft Copilot Studio and agentic AI solutions. Azure AI and Machine Learning technologies. Working closely with AI-focused delivery teams and AI initiatives. Coaching and mentoring junior engineers. Data platform strategy and enterprise data ...

Fabric platform architecture and governance. Cloud data platform design and strategic architecture. AI Agents, Microsoft Copilot Studio and agentic AI solutions. Azure AI and Machine Learning technologies. Working closely with AI-focused delivery teams and AI initiatives. Coaching and mentoring junior engineers. Data platform strategy and enterprise data ...

Fabric platform architecture and governance. Cloud data platform design and strategic architecture. AI Agents, Microsoft Copilot Studio and agentic AI solutions. Azure AI and Machine Learning technologies. Working closely with AI-focused delivery teams and AI initiatives. Coaching and mentoring junior engineers. Data platform strategy and enterprise data ...

Fabric platform architecture and governance. Cloud data platform design and strategic architecture. AI Agents, Microsoft Copilot Studio and agentic AI solutions. Azure AI and Machine Learning technologies. Working closely with AI-focused delivery teams and AI initiatives. Coaching and mentoring junior engineers. Data platform strategy and enterprise data ...

trained efficiently, deployed securely, and operate at scale. This role sits within the Machine Learning function and works closely with ML Scientists, DevOps, Data Engineering and Software Engineering teams to turn research models into robust, scalable and reproducible training and inference workflows. What you will do Develop robust … large-scale model training. Act as a bridge between research and production, translating research into reliable, maintainable and scalable engineering components. Support cloud-based ML infrastructure such as MLFlow. Create and maintain CI pipelines for model training, testing and deployment workflows. Collaborate with DevOps and infrastructure teams on deployment patterns ...

clients. Join our engineering teams that build massively scalable software and systems, architect low latency infrastructure solutions, proactively guard against cyber threats, and leverage machine learning alongside financial engineering to continuously turn data into action. Create new businesses, transform finance, and explore a world of opportunity ...

trained efficiently, deployed securely, and operate at scale. This role sits within the Machine Learning function and works closely with ML Scientists, DevOps, Data Engineering and Software Engineering teams to turn research models into robust, scalable and reproducible training and inference workflows. What you will do Develop robust … large-scale model training. Act as a bridge between research and production, translating research into reliable, maintainable and scalable engineering components. Support cloud-based ML infrastructure such as MLFlow. Create and maintain CI pipelines for model training, testing and deployment workflows. Collaborate with DevOps and infrastructure teams on deployment patterns ...

valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen … regulatory controls, and with vulnerability patching across base OS images, networking and IAM Experience with Bazel, Nix and other similar technologies Experience serving ML models in production, or building data and real-time analysis pipelines Experience working on projects related to national security for one or more government agencies Prior ...

patterns across real-time market data platforms. Support high-performance platforms for tick data storage, market replay, order book reconstruction, quantitative analytics, and future machine learning use cases. 5. Governance, Controls, Vendor & Stakeholder Engagement Champion data quality, lineage, monitoring, entitlement management, ownership, governance, and operational controls across real ...

handle high-pressure production incidentsDesiredExperience with Site Reliability Engineering (SRE) practices, including monitoring, incident response, and post-mortem analysisProven experience applying AI or machine-learning models to optimise workflows, identify patterns, and drive intelligent automation solutionsHands-on experience with containerization technologies such as Docker and Kubernetes, including cluster ...

pressures production incidents Desired Experience with Site Reliability Engineering (SRE) practices, including monitoring, incident response, and post-mortem analysis Proven experience applying AI or machine-learning models to optimise workflows, identify patterns, and drive intelligent automation solutions Hands-on experience with containerization technologies such as Docker and Kubernetes ...

systems integration Event-driven and service-oriented architecture Data platforms, analytics and business intelligence Data governance, quality, lineage and master data Artificial intelligence, machine learning and automation Identity and access management Cybersecurity, privacy and compliance DevOps, DevSecOps and platform engineering Containers, Kubernetes and infrastructure as code Enterprise service ...

join our team. You will report to a Leader of Engine Development and collaborate with game teams, central technology engineers, and researchers to bring ML models into the hands of our players by deploying them directly within EA games. Responsibilities: Evaluate, adapt, extend, and improve existing machine learning runtime technologies, while designing and implementing new solutions from the ground up when needed. Partner with ML experts across EA to assess, apply, and scale models and architectures for on-device performance. Translate ML concepts, tradeoffs, risks, and technical constraints into clear language for different audiences. Build ...

core of how Trainline is transforming travel, helping millions of customers make smarter, more sustainable journeys every day. Our ML models and AI solutions power critical aspects of our platform, including advanced search and recommendations across our mobile and web applications, pricing and routing optimisation, personalised experiences enhanced by agentic … help shape data products that make good use of Trainline's extensive datasets Contribute to the tools, frameworks and libraries that help accelerate our ML and AI delivery and improve our workflows Take part in our AI and ML community, sharing learning and building your experimentation skills alongside others ...

defining best practices. Working with clients, and cross-functional teams, you’ll ensure technical feasibility and timely delivery of high-quality, production-grade ML systems. What you’ll be doing: Building and deploying production-grade ML software, tools, and infrastructure. Creating reusable, scalable solutions that accelerate the delivery of ML … implementing Faculty’s standards for deploying machine learning at scale. Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders. Who we’re looking for: You understand the full machine learning lifecycle and have experience operationalising models built with frameworks like ...

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 ...

intelligent systems are built and delivered at JPMorganChase. In this role, you’ll contribute to the LLM Suite platform by building AI/ML and agentic capabilities that are secure, reliable, and ready for production. You’ll collaborate closely with senior engineers, learn through real design discussions, and grow your … in. As a Software Engineer, Senior Associate, in the LLM Suite Engineering team, you will design, build, and troubleshoot software that enables AI/ML and agentic experiences on the platform. You will write secure, high-quality code and support algorithms that integrate with existing systems. You will collaborate with ...

clients, hands-on with model integration, multi-agent workflows and data engineering from day one. Looking for a strong Python and SQL foundation, basic ML knowledge, and a CS/AI/Data Science degree with solid academic results. UK (Brighton, Hybrid), Europe (Remote), United Kingdom Contract Location: UK (Brighton … with hands-on model integration, agentic workflows and data engineering from day one. We're looking for a strong Python and SQL foundation, basic ML knowledge, and a CS/AI/Data Science degree with solid academic results. Brighton (Hybrid), Europe (Remote), United Kingdom Full Time Location: Brighton (Hybrid ...

provide technical leadership across the full lifecycle of machine learning systems powering a new matching platform. This role is accountable for defining ML architecture, establishing engineering standards, driving MLOps maturity, and ensuring that our models are scalable, secure, explainable, and governed to enterprise‐grade standards. You will contribute … strategic direction of our ML platform—spanning data pipelines, model development, deployment automation, inference runtime design, telemetry, drift detection, and cross‐account productionisation. You will mentor engineers, influence product and architectural decisions, and ensure that our ML systems operate reliably at scale, underpinned by a robust governance and compliance framework. ...

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/ ...

forward-deployed senior engineer who unlocks AI-driven solutions to business problems: an expert in deploying AI and using it safely, not an ML modeller. The work is mainly Python across the modern AI engineering stack - harness engineering, skills and tool building, agent workflows and orchestration, agent hosting and sandboxing … engagement and transforming working patterns into load-bearing, AI-enabled business processes. Customer-facing AI deployment. Deploy and integrate AI components built by our ML and Decisioning teams into production: the engineering implementation layer between a working model and a live customer feature. AI platform capabilities. Work with ...

forward-deployed senior engineer who unlocks AI-driven solutions to business problems: an expert in deploying AI and using it safely, not an ML modeller. The work is mainly Python across the modern AI engineering stack - harness engineering, skills and tool building, agent workflows and orchestration, agent hosting and sandboxing … engagement and transforming working patterns into load-bearing, AI-enabled business processes. Customer-facing AI deployment. Deploy and integrate AI components built by our ML and Decisioning teams into production: the engineering implementation layer between a working model and a live customer feature. AI platform capabilities. Work with ...

Model ML is the AI workflow builder transforming how major financial institutions produce and validate client-ready work. Model ML converts complex, manual processes into fully automated AI systems that scale across global teams. In under a year, Model ML has become one of the fastest growing enterprise AI platforms ...