576 to 600 of 638 MLOps Jobs in the UK

production AI and agentic workflows across the organisation. You will operate at a senior technical level, working across Azure infrastructure, AI platform engineering, security, MLOps, FinOps and AI engineering teams to create a secure, scalable and reusable AI platform. The successful candidate will combine deep Azure architecture expertise with hands … consumption‐based AI costs. Work with engineering and finance stakeholders to identify opportunities to optimise AI platform expenditure without compromising performance or service quality. MLOps, Automation & Platform Engineering Design and implement automated deployment pipelines for AI platform components and workloads. Establish CI/CD processes using Azure DevOps and/ ...

About Unifeye Unifeye is a delivery-led, Databricks-first consultancy helping organisations unlock the full value of their data and AI. We're a lean, high-growth team based in London, working at the intersection ...

ML Ops Engineer Role Overview We’re hiring an ML Ops Engineer to own the reliability, scalability, and operational integrity of our machine‐learning systems in research & production. This role sits at the intersection of ...

At Gigaton, we’re on a mission to cut gigatonnes of carbon emissions from the world’s biggest emitting industries (like cement, steel and glass), by building autonomous AI control and optimisation systems that learn ...

Trainline in London is seeking an experienced MLOps Engineering Manager to build and lead a new team of engineers. You will shape deployment, observability, and scalable machine learning systems across the platform. You will collaborate with ML Engineers, Data Engineers, Software Engineers, Data Scientists, Product Managers and stakeholders to deliver ...

guide enterprise customers in building production-grade ML/AI apps on the Databricks Data Intelligence Platform. You will mentor peers and advance GenAI, MLOps, and LLMOps while staying at the forefront of AI tech. You will work with Solution Architects to design end-to-end pipelines, optimize training ...

will partner with engineering, data, security and risk teams to enable reliable model development, deployment and monitoring in a regulated environment. Strong software and MLOps skills are essential. #J-18808-Ljbffr ...

trusted technical expert for customers and Field Engineering. You will guide enterprise workloads on the Databricks Data Intelligence Platform, focus on GenAI, ML, MLOps, and LLMOps, and mentor peers while advancing the AI roadmap. You will engage with Solution Architects to design production-grade ML/AI solutions, deliver MVPs ...

applications on the Databricks Data Intelligence Platform, while mentoring peers and shaping AI thought leadership. This role requires deep hands-on experience with ML, MLOps, GenAI, and a passion for sharing knowledge and driving #J-18808-Ljbffr ...

leadership across the full lifecycle of machine learning systems while architecting production-grade ML solutions. You will work with AWS SageMaker, lead teams on MLOps strategies, and ensure systems adhere to governance standards. This is a hands-on, technical position ideal for those with deep expertise in ML frameworks such ...

world. This is a role for someone who shows up with substance, who can hold a real conversation about model training and MLOps tooling in the morning and cluster networking in the afternoon, and who finds the space between infrastructure and application more interesting than either on its own. About … Account Solution Architect covering Northern EMEA, you’ll move across the full stack depending on what the customer needs, from ML training workflows and MLOps platforms like Weights & Biases to bare metal GPU infrastructure and physical AI deployments. This isn’t a role built around a single layer ...

data solutions. This role will see you building and maintaining scalable platform capabilities, troubleshooting existing services, and providing technical support to Data Scientists and MLOps teams working across a range of technologies. Key Responsibilities of the Analytics Platform Engineer Design, build and maintain scalable analytics platforms and cloud-based infrastructure. … platform capabilities while enhancing and maintaining existing analytics solutions. Troubleshoot technical issues, implement improvements and optimise platform performance. Collaborate closely with Data Scientists, MLOps Engineers and other technical teams to deliver reliable, secure and high-performing solutions. Adapt to a varied workload, solving complex engineering challenges across a range ...

manage cloud capacity, including CPU, GPU, storage and networking resources, balancing business needs, platform performance and cost efficiency. Build and support infrastructure for MLOps processes, including CI/CD pipelines, experiment tracking, model registries, automated workflows and model deployment. Develop reusable automation and platform capabilities that simplify onboarding, reduce manual … requirements into effective cloud and platform solutions. Create and maintain technical documentation, support onboarding activities and contribute to the evaluation of new cloud and MLOps technologies. Requirements Hands-on experience designing, implementing and supporting cloud infrastructure in AWS environments. Strong knowledge of AWS services including SageMaker, IAM, networking, storage, compute ...

workflows. You optimize the computational stack for performance, cost, power, and scalability, monitor AI systems and infrastructure health across both InfraOps and MLOps disciplines, perform AI monitoring to track model and system performance, and independently troubleshoot and resolve complex issues across the stack. You also mentor junior engineers, contribute … driven role where you apply and deepen your expertise across modern tools and platforms — including container orchestration, model serving, CI/CD pipelines, InfraOps, MLOps, and AI monitoring — while making meaningful contributions to infrastructure that enables AI-driven business outcomes. THE WORK Write, review, and debug code, scripts, and infrastructure ...

preprocessing, model training (PyTorch, TensorFlow), evaluation against business or research metrics, and production deployment (FastAPI, Triton, Ray) with monitoring. The role increasingly overlaps with MLOps (model deployment, monitoring, retraining infrastructure) and applied ML research, taking new techniques from papers to production. Generative AI (LLMs, diffusion models) is the most active … 2026. Responsibilities Train, evaluate, and deploy machine‐learning models in production. Work across LLMs, computer vision, recommender systems, and forecasting. Specialise into ML research, MLOps, applied ML, or generative AI. Work for UK AI labs (DeepMind, Anthropic), fintechs, scale‐ups, and major corporates. Skills & Qualifications Reading and implementing academic papers. ...

enterprise sales cycles and co‐own the technical win Lead deep technical discovery sessions with a wide range of stakeholders (from ML Engineers to MLOps leaders) to not only uncover business pain but to architect a complete technical solution and integration path Architect, build, and deliver highly‐technical, customised product … delivery in a complex enterprise sales environment Proven track record of earning trust with highly technical audiences (ML engineers, MLOps, data engineers) while communicating value clearly to senior business stakeholders Experience navigating enterprise security, compliance, and architecture reviews as part of a technical sales or implementation process Prior experience ...

deploy automated pipelines for ML solutionsLeverage foundational libraries and services for re-use across teamsApply best practices in software engineering and computer scienceUtilize MLOps tools for versioning, reproducibility, and observabilityAlign ML problem definitions with business objectivesMentor and support team members, with optional management responsibilitiesRequired Qualifications, Capabilities, and Skills: Experience … testing, and deployment pipelinesFamiliarity with higher-level interfaces like Pydantic AI and LangraphStrong understanding of computer science fundamentals and development best practicesBroad knowledge of MLOps tooling for versioning, reproducibility, and observabilityAbility to understand business objectives and align ML problem definitionsPreferred Qualifications, Capabilities, and Skills: Experience mentoring or leading teamsKnowledge ...

retention, ARPU, ROAS, and operational efficiency. Deliver robust, production-ready ML systems across multiple verticals. Leverage AWS SageMaker, Snowflake, and internal tools with strong MLOps practices. Build and manage a high-performing AI/ML team of 10–20 professionals. Promote a culture of ownership, delivery excellence, and continuous team … technical audiences. Hands-on experience with user-level and performance marketing data.• Proficiency with AWS (SageMaker, Lambda, Step Functions), Snowflake, and modern MLOps tooling. Familiarity with ML frameworks including PyTorch, TensorFlow, scikit-learn, and XGBoost. Experience managing and scaling high-performing data science and ML engineering teams. Working at Tripledot25 ...

pipelines for ML solutions Leverage foundational libraries and services for re-use across teams Apply best practices in software engineering and computer science Utilize MLOps tools for versioning, reproducibility, and observability Align ML problem definitions with business objectives Mentor and support team members, with optional management responsibilities Required Qualifications, Capabilities … pipelines Familiarity with higher-level interfaces like Pydantic AI and Langraph Strong understanding of computer science fundamentals and development best practices Broad knowledge of MLOps tooling for versioning, reproducibility, and observability Ability to understand business objectives and align ML problem definitions Preferred Qualifications, Capabilities, and Skills: Experience mentoring or leading ...

accuracy, and consistency of training data across multiple concurrent projects and robot platforms. Collaborate with machine learning teams to shape the Capability Factory, streamline MLOps, and build the evaluation workflows that close the loop between training runs and real-world robot performance. Build data warehouse solutions and BI dashboards that … been on-call, triaged incidents under pressure, and improved systems after postmortems. You take reliability personally. Nice to Have ML infrastructure or MLOps experience - understanding of how training jobs run, how model artifacts are managed, and what makes an evaluation pipeline trustworthy; you\'ve worked alongside or directly supported ...

business. Bridge our Machine Learning, Product and Platform Engineering capabilities We have an extremely talented pool of full-stack machine learning and MLOps engineers and an exceptional platform engineering team. We need software 'glue' expertise and capability to bring these two things together optimally. With our other software engineers … startup or small team Are confident using AWS and Kubernetes Experience with workflow orchestration systems (e.g. Temporal) Can venture into Data Engineering, MLOps and/or Platform Engineering About us Unitary is a remote‐first team of 25+ people spread across Europe who are fiercely passionate about making the internet ...

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 … 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 ...

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 … 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 ...

Title: MLOps Platform Developer Location: London Salary: Depending on experience Job Type: Full time, Permanent This is a rare opportunity to be first in the door to continue the development of our engineering platform. You will own a production system end to end the web application, the database estate ...

Hudl is hiring a Senior MLOps Engineer for our Hardware Group to build and scale edge ML infrastructure powering Focus cameras. You will own edge deployment pipelines delivering models to thousands of devices globally and contribute to a platform that compiles models into hardware-specific inference engines. You will work ...