501 to 525 of 635 MLOps Jobs in the UK

hybrid SQL/Python architecture (dbt, Snowflake) to extract rich, localized temporal signals from high-volume transactional retail data. End-to-End Deployment (MLOps): Partner with our engineers to transition your models from prototype into scalable, monitored, and reliable production pipelines using tools like MLflow and Optuna. Your Skills Strong … transformation tools like dbt. Cloud & Data Infrastructure: Experience working with scalable cloud infrastructure (AWS, GCP, or Azure), and handling columnar data formats like Parquet. MLOps Exposure: Experience with tools and frameworks for training, tracking, deploying, and monitoring models in production (e.g., MLflow, Optuna, Prefect, or Airflow). Background in supply ...

hybrid SQL/Python architecture (dbt, Snowflake) to extract rich, localized temporal signals from high-volume transactional retail data. End-to-End Deployment (MLOps): Partner with our engineers to transition your models from prototype into scalable, monitored, and reliable production pipelines using tools like MLflow and Optuna. Your Skills Strong … transformation tools like dbt. Cloud & Data Infrastructure: Experience working with scalable cloud infrastructure (AWS, GCP, or Azure), and handling columnar data formats like Parquet. MLOps Exposure: Experience with tools and frameworks for training, tracking, deploying, and monitoring models in production (e.g., MLflow, Optuna, Prefect, or Airflow). Desirable Background ...

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. ...

production environment — not just notebooks. You've owned models after they ship and know how to keep them healthy. Full‐Lifecycle Delivery (MLOps): Comfortable delivering the end‐to‐end production lifecycle — model training, versioning, monitoring, and champion/challenger experimentation — without relying on a dedicated ML engineering team to carry … what to do about it. [Bonus] Experience with VertexAI TOOLS & TECHNOLOGIES Languages: Python, SQL Data & Transformation: dbt, Snowflake, BigQuery Visualisation & BI: Looker Engineering & MLOps: Docker, GitHub Workflow & Orchestration: Vertex AI Pipelines (GCP), Kubeflow LLMs & GenAI: Gemini API, Claude API INTERVIEW PROCESS Step 1: People Team Screening Call (30 min) Step ...

Req ID:333444NTT DATA strives to hire exceptional, innovative and passionate individuals who want to grow with us. If you want to be part of an inclusive, adaptable, and forward-thinking organization, apply now. We ...

POSITION We are seeking a senior leader inMLOpsEngineeringto join Computational Innovation's AI Accelerator (@computationalinnovation). In this role, you will own the MLOps architecture, standards and technical direction for the AI Accelerator, ensuring that models can move efficiently from experimentation to reliable production deploymentand support. You will define … working and coach other team members, onboarding, mentoring and technically leadingMLOpsengineers as the team grows, while acting as the senior escalation point for complex MLOps challenges. Requirements PhD orMSc and equivalent experience in a STEM subject. Extensive experience operating atsenior stafflevel within anMLOps, ML Platform Engineering or Machine Learning Engineering ...

should have 5+ years in data and AI, with at least 2+ years in consulting, plus a hunger to learn, apply data governance, MLOps, GenAI, cloud strategy and FinOps. #J-18808-Ljbffr ...

into scalable architectures and ensure integration with the client’s IT and data ecosystem. You will guide multidisciplinary teams through prototyping to production, establish MLOps practices, and uphold security, privacy, and explainability while aligning with enterprise architecture standards. #J-18808-Ljbffr ...

deliver an enterprise-grade Microsoft Azure AI Foundry platform in a Leeds-based environment. You will operate across Azure infrastructure, AI platform engineering, security, MLOps and FinOps to establish technical standards and scalable, reusable patterns for AI adoption. The role emphasizes governance, cost management and hands-on engineering leadership ...

engine for ranking, retrieval, and recommendations. You will define the ML strategy, build scalable systems, and mentor a high-performing team across machine learning, MLOps, and experimentation. You will drive multi-task models, graph-aware representations, and agentic AI approaches to improve user search, discovery, and itineraries at scale within ...

Strong working knowledge of ISO 27001 (Information Security Management) and ISO 42001 (Artificial Intelligence Management System). Hands-on understanding of data management principles, MLOps lifecycles, and model auditing. Must have active UK SC clearance (or active DV clearance). #J-18808-Ljbffr ...

practice and drive improvements across our ML operations. The ideal candidate will have the following skills and experience: An appreciation of good architecture and MLOps best practiceThe ability to collaborate with, and influence, technical and non-technical peopleA passion for end-to-end ownership of solutions, from articulation to deliveryProven ...

executives. 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 … Work (SOWs) and solution proposals for complex data migration, platform modernization, and AI integration initiatives that are commercially sound and technically viable. Data Quality & MLOps Governance: Establish rigorous standards for data quality, data lineage, automated testing, and MLOps/DataOps practices to guarantee model reliability and pipeline performance. Programme Governance ...

Tune machine learning methods to best leverage our state-of-the-art processing capabilities Deploy and maintain machine learning methods in a DevOps/MLOps based machine learning environment Create robust high-quality code using test-driven development (TDD) techniques and adhering to the SOLID coding standards Your work will … Director of Technical Underwriting Propose, proof-of-concept, develop, and deliver novel machine learning processes that automate current manual processes, and leverage DevOps and MLOps software. Work in a collaborative environment with data science to help deploy machine learning methods that are state-of-the-art, robust, and future extensible. ...

into production-ready features that feed our dispute systemsCollaborate with platform and backend engineers to integrate models seamlesslyExperience and qualifications5+ years of experience as MLOps/ML EngineerHigh proficiency in writing clear, production-ready Python codeExperience with production ML models (online or offline) and standard MLOps practicesExperience with monitoring ...

Engine behind the Syrup platform and Anaplan’s forecasting solutions. The team is responsible for the production execution of forecasting and predictive models, the MLOps infrastructure supporting our data scientists, and the data processing services that deliver insights to enterprise customers. This role reports to the Director of Engineering … implement scalable, fault-tolerant predictive intelligence services and features as a core contributor to the ML Engine backend. Lead the technical implementation of MLOps capabilities, including model training pipelines, deployment workflows, and runtime infrastructure for production ML. Partner with data scientists to productionize models and graduate experimental work into stable ...

Experience fine‐tuning LLMs (e.g., LoRA, QLoRA, RLHF) specifically for domain‐specific enterprise applications. Taken responsibility of models from concept to production, utilising strong MLOps and LLMOps practices to ensure scalable, reliable, and monitorable deployments. High proficiency in Python and modern software development practices, including rigorous testing, code reviews … Computer Science, Artificial Intelligence, Statistics, Data Science, or a related quantitative field. Experience working with scalable cloud infrastructure (AWS, GCP, or Azure) and MLOps tools for model training, monitoring, and deployment. Background in forecasting, planning, or analytics applications. Familiarity with Anaplan or similar enterprise planning platforms. Experience with A/ ...

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 ...

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 ...

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 ...

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 ...

infrastructure that makes world-class AI possible. Working closely with the Applied AI team, you'll be building and maintaining the data science, MLOps, and deployment tooling that empowers our team of over 100 Data Scientists and Engineers. You will take ownership of the platform that enables us to transition … solutions are high-performing, scalable, and seamlessly integrated into diverse client environments. What you'll be doing: Taking ownership of our existing deployment and MLOps tooling to ensure our software delivery remains a significant lever for quality and reliability. Contributing to the continuous evolution of our technology stack, from building ...

/CDStrong problem-solving, communication, and collaboration skills, with the ability to convey design choices and communicate effectively with stakeholdersFamiliarity 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 … containerized application developmentFamiliarity with relational databases (e.g., Postgres) and AWS services such as S3, EKS, SageMaker, and BedrockPractical 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 scientistsJ.P. Morgan is a global leader ...

automate regulatory evidence generation to reduce time to submission Own cloud-side data pipelines and infrastructure-as-code, supporting data ingest, analysis, and MLOps workflows where required Coordinate cybersecurity workstreams, working closely with IT and QARA teams Partner with the Software Engineering Manager on release processes, branching strategy, and automated … cloud provider (AWS, GCP, or Azure) Nice to have: Experience with hardware-in-the-loop or software-in-the-loop test environments Exposure to MLOps, including model drift validation, dataset governance, and automated performance evaluation and deployment of organisational AI strategy Experience with cybersecurity requirements (e.g. ...

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 ...