301 to 325 of 409 MLOps Jobs in London

more cost-effective to ship than the last. Working closely with our Data Scientists and Data Engineers, you’ll help shape how we do MLOps at PureGym, bringing strong software engineering practices into the modelling lifecycle and making it easier for the team to get great models into production. What … Turn models and metric definitions created by Data Scientists into robust, reusable production components on our shared semantic layer. Help define and evolve our MLOps standards, covering versioning, model registry practices, documentation and handover. Partner with Data Engineers on upstream pipelines and Data Scientists on model design, ensuring production requirements ...

institutional sponsorship enables Radiant to pursue large-scale GPU and AI infrastructure opportunities globally. Job Summary: As a Senior/Principal Product Manager - MLOps, you will own the strategy, roadmap, and delivery of Radiant’s machine learning platform and suite of AI/ML cloud services. You’ll work across … build, train, deploy, and operate ML workloads on Radiant’s neocloud platform. Key Responsibilities: Own the strategy, roadmap, and delivery of Radiant’s MLOps and ML platform services. Define products across the ML lifecycle, including training, fine-tuning, model management, deployment, inference, and monitoring. Translate customer needs into clear product ...

About H: H exists to push the boundaries of superintelligence with agentic AI. By automating complex, multi-step tasks typically performed by humans, AI agents will help unlock full human potential. H is hiring the ...

seeking an AI Engineer in London to design, build and deploy machine learning solutions for enterprise clients, spanning model selection, data pipelines, DevOps/MLOps and production-grade AI services. You will work with Python, C++, and frameworks like TensorFlow and PyTorch, on cloud platforms and scalable data architectures, with ...

engineering industry, delivering the performant, reliable, and trustworthy data foundation trusted by the world’s largest engineering companies. About The Role As an MLOps Engineer on the Physical AI team, you will serve as the hands‐on owner for our machine learning operations surface across the end‐to‐end model … lifecycle—from experimentation and training through to packaging, deployment, serving, and retirement. You will define and roll out MLOps practices, establish operational SLOs/SLAs, and build automated CI/CD and continuous training pipelines to accelerate the path from experiment to supported production deployment. In this role, you will ...

seeking an AI Platform & Site Reliability Engineering Managing Consultant to design, build and scale secure AI platforms. You will integrate SRE, observability and MLOps, guiding senior clients from strategy to delivery. This role offers flexible hybrid working across Manchester, Glasgow or London, with leadership of multidisciplinary teams and governance shaping ...

. You will bridge the gap between complex enterprise data strategies—spanning Cloud Migrations, Data Mesh architectures, Real-Time Analytics, and Generative AI/MLOps pipelines—and precise execution across globally distributed engineering pods of 100+ people. The Impact By pairing technical depth with executive gravitas, you will de-risk … Fluency: Deep understanding of modern data stacks (Data Lakehouses, Data Mesh, Real-time Streaming, ETL/ELT pipelines) and AI/ML lifecycle management (MLOps, GenAI integration, Model Governance). Ability to challenge architectural patterns and supervise technical teams without acting as the hands-on architect. Cloud & Platform Mastery: Expertise ...

Analysis: Apply established data science and machine learning techniques to analyse data and solve business problems under the guidance of senior team members. Production & MLOps: Build, test, and help deploy machine learning models into production environments. Contribute to the design and maintenance of MLOps pipelines to ensure solutions are scalable ...

data quality, governance, and reliability. Develop multimodal AI models that combine visual, textual, and customer data to deliver enhanced automation and personalisation capabilities. Apply MLOps best practices across the AI lifecycle, including CI/CD, monitoring, model deployment, testing, and performance optimisation. Collaborate with product, engineering, and cloud teams … Prompt Engineering, Retrieval Augmented Generation (RAG), and model fine-tuning techniques. Understanding of modern software engineering practices including Git, CI/CD, Containerisation, and MLOps methodologies. Experience delivering end-to-end machine learning projects from concept through to production deployment. Strong understanding of AI evaluation frameworks, model testing, benchmarking ...

customers. They will be subject matter experts in the areas of Predicative AI, Machine Learning, Generative AI along with associated capabilities in areas like MLops, Data Pipelines, and DevOps. The role will interface directly with the customers' business, data science and IT Operations teams to establish CDW credibility and position … learning, lab time and formal training. What you will do Position CDW Custom AI capabilities - Engage with all Customer AI personas: technical personas (CDO, MLOps, AI/ML Engineer, Data Scientist), outcome-driven personas (CEO, CTO, LOB) and ITDMs (CIO, VP Infrastructure). Technical SME on CDW Custom AI – Maintain ...

Depop is looking for a Machine Learning Engineer to join the Ranking team in the UK. You will work alongside ML Scientists, Backend Engineers, MLOps, and other ML Engineers to build, deploy, maintain, and monitor the machine learning systems that power personalised ranking across key surfaces of the Depop … product teams to define integration requirements and coordinate deployment of ranking services. Help extend the ML infrastructure for ranking systems in collaboration with the MLOps team, including: Reproducible model training workflows CI/CD pipelines for model deployment Real-time and batch model serving Online/offline feature consistency through ...

Azure-based data and AI platform. This is hands‐on infrastructure and platform work – you'll be writing Terraform, designing network architecture, implementing MLOps (AI model) pipelines, and establishing DevSecOps patterns that engineering teams can use. This isn't a “thought leadership” or “strategy” role. … maintain CI/CD for Databricks notebooks, jobs, and infrastructure Work with data engineers on performance optimization, cost control, and platform capabilities MLOps & AI Platform (20%) Build ML model deployment pipelines using Azure ML, Databricks MLflow, or both Implement model versioning, experiment tracking, and model registry patterns Establish inference endpoints ...

including generative AI and agentic systems, on cloud infrastructure. You will own end-to‐end delivery from problem framing and experimental design through scalable MLOps, evaluation, governance, and integration with strategic systems. You will help set technical direction, establish reusable patterns, and ensure solutions are reliable, secure, and observable … reliability, incident management, and continuous improvement Strong Python software engineering skills, including modular object‐oriented design, testing, performance tuning, and debugging Working knowledge of MLOps and LLMOps and distributed systems, including training and serving patterns, batch versus real‐time architectures, feature stores, orchestration, and scalable data processing Ability to design ...

lifecycle — assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and DevOps & MLOps pipelines for training and production, and ensuring quality, value, and reliability of deployed systems. THE WORK Formulate real-world problems into practical, efficient, and scalable … Develop and implement machine learning algorithms, models, and computational systems; design and build scalable data pipelines to support model training and production with DevOps & MLOps Customize and apply Deep Learning and Gen AI models for various use cases based on the business needs, data availability, system and infrastructure requirements - including ...

communicate complex findings to both technical and executive stakeholders. Key Responsibilities Design, implement and evolve a multi-omics AI platform, incorporating full-lifecycle MLOps Identify, ingest, link and curate data across large-scale multi-omics and imaging datasets, including data quality, validation, traceability and storage optimisation Standardise and automate … experience in a fast-paced environment, preferably a startup, with the ability to iterate, experiment, and deliver solutions efficiently. Practical experience across the entire MLOps lifecycle for cloud, hybrid and on-premise deployments. Adaptability and comfort working with evolving requirements and changing priorities, approaching new challenges with curiosity and openness. ...

Deloitte is seeking an experienced MLOps Engineer for a contract role in London, hybrid 3/4 days in client office. You will design, develop and deploy production-ready AI agents and scalable ML pipelines in a regulated financial services environment. The role covers Generative AI, retrieval systems, and enterprise ...

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 ...

solution lifecycle — assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and MLOps/LLMOps pipelines for training and production, and ensuring quality, value, and reliability of deployed systems. THE WORK Formulate real-world problems into practical, efficient … Develop and implement machine learning algorithms, models, and computational systems; design and build scalable data pipelines to support model training and production with DevOps & MLOps Customize and apply Deep Learning and Gen AI models for various use cases based on the business needs, data availability, system and infrastructure requirements - including ...

Strong problem-solving, communication, and collaboration skills, with the ability to convey design choices and communicate effectively with stakeholders Familiarity with Machine Learning Operations (MLOps) Hands‐on experience using enterprise‐authorized AI‐assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated … development Familiarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and Bedrock Practical experience with Kubernetes, EKS, Docker, Kafka, MLOps and Large Language Model Operations (LLMOps) Experience working on AIML systems and/or prior experience collaborating with data scientists #J-18808-Ljbffr ...

lifecycle — assessing client needs and data, selecting and customizing models (including Deep Learning, Generative AI, and Large Language Models), designing scalable data and DevOps & MLOps pipelines for training and production, and ensuring quality, value, and reliability of deployed systems. THE WORK Formulate real-world problems into practical, efficient, and scalable … Develop and implement machine learning algorithms, models, and computational systems design and build scalable data pipelines to support model training and production with DevOps & MLOps Customize and apply Deep Learning and Gen AI models for various use cases based on the business needs, data availability, system and infrastructure requirements - including ...

business stakeholders to identify opportunities for optimization, forecasting, automation and intelligent decision support. Support the deployment of machine learning models into production environments using MLOps practices. Work with structured, semi-structured and streaming data to generate actionable insights; Contribute to AI-enabled solutions including Generative AI, Agentic AI, NLP, forecasting … Data Scientists and other analytical specialists to translate analytical approaches into production solutions. Experience developing production-grade user experiences and data visualizations. Experience with MLOps platforms and model lifecycle management. Familiarity with optimization, forecasting and simulation techniques. Experience building or contributing to AI-enabled operational products. Exposure to Generative ...

functional agile team and owning the successful launches and ongoing operations of high-profile products in production. This is a senior software engineering/MLOps role focused on building and operating production services and platform capabilities, not a model research role. Data science experience is valuable but secondary to production … engineers and data scientists, mentor teammates, and drive continuous improvement and knowledge sharing. What you’ll bring 5+ years as an software engineer, MLOps Engineer, or similar, with hands-on production experience operating backend services or ML-backed APIs Proven expertise deploying ML models as production services, API design ...

solutions. Acting as a key technical leader and cross-functional integrator, the Senior AI Platform Engineer partners with research, product, infrastructure, data engineering, and MLOps teams to design and operate the platforms required to train, evaluate, deploy, and govern large-scale AI systems across IQVIA products and healthcare use cases. … driving technology selection, migration strategies, performance optimisation, and integration with AI workflows. Serve as the primary technical coordination point across AI Research, Data Engineering, MLOps, Product, and Infrastructure teams, resolving dependencies and prioritising activities critical to delivery. Provide technical leadership for vendor selection, procurement, and technology partnerships, advising on compute ...

Platform Strategy & Architecture: Assess, define and evolve enterprise AI platform architectures, covering LLM and agentic frameworks, AI gateways, model lifecycle management, data platforms, MLOps/LLMOps foundations and integration patterns. Support clients in evaluating build, buy and hybrid approaches aligned to business needs, risk appetite and operational requirements.• AI Platform … operating cloud-native, platform engineering, AI platform or reliability engineering solutions within complex enterprise environments. • Strong understanding of AI platform architectures including LLMOps, MLOps, agentic AI frameworks, model lifecycle management and AI operational controls. • Experience establishing and scaling SRE practices including observability, SLIs, SLOs, error budgets, incident management and reliability ...

Join JPMorgan Corporate Investment Bank's industry-leading data analytics team, where you'll combine cutting-edge machine learning techniques with unique data assets to optimize business decisions. As an Applied AI & Machine Learning Associate ...