326 to 350 of 433 MLOps Jobs in London

ensuring governance, safety, and human-in-the-loop decision-support for MoD stakeholders and prime contractors. Responsibilities include edge optimization, graph processing, AI governance, MLOps in low-bandwidth #J-18808-Ljbffr ...

Reliably fitting models with complex job dependency graphs Robustness in production, including validation and monitoring Qualifications An appreciation of good architecture and MLOPs best practice The ability to collaborate with, and influence, technical and non‐technical people A passion for end‐to‐end ownership of solutions, from articulation to delivery ...

Experience applying AI both to front-office decision-making and to how change and operations are delivered. Strong grasp of the modern AI ecosystem, MLOps, evaluation and monitoring, and responsible-AI practice. AI governance experience and the ability to make data fit for AI, working through certified data products ...

solutions from concept to production Develop applications using LLMs, Generative AI and Agentic AI frameworks Deliver predictive, optimisation and automation use cases Build robust MLOps and LLMOps processes, including monitoring and evaluation Work closely with engineers, data scientists, product teams and business stakeholders Help establish AI engineering standards, governance … software engineering background Commercial experience delivering machine learning models into production Hands-on experience with GenAI, LLMs and AI application development Knowledge of MLOps, LLMOps, CI/CD and containerisation Experience with cloud platforms, ideally Azure Understanding of model evaluation, monitoring and responsible AI Strong communication skills with the ability ...

infrastructure changes. Azure DevOps Pipelines : Design, build, and maintain secure, high‐speed CI/CD pipelines within Azure DevOps to automate code promotion and MLOps workflows. Incident Management : Lead the response, containment, and resolution of critical platform outages, serving as a primary escalation point for high‐priority cloud and infrastructure … requirements, post‐mortems, and engineering strategies effectively to both technical and non‐technical stakeholders. AI/ML Platform Automation : Architect and maintain highly available MLOps platforms across AWS and Azure environments. Developer Enablement : Build self‐service infrastructure tools, deployment templates, and continuous training/inference pipelines to eliminate friction. ...

infrastructure changes. Azure DevOps Pipelines : Design, build, and maintain secure, high-speed CI/CD pipelines within Azure DevOps to automate code promotion and MLOps workflows. Incident Management : Lead the response, containment, and resolution of critical platform outages, serving as a primary escalation point for high-priority cloud and infrastructure … requirements, post-mortems, and engineering strategies effectively to both technical and non-technical stakeholders. AI/ML Platform Automation : Architect and maintain highly available MLOps platforms across AWS and Azure environments. Developer Enablement : Build self-service infrastructure tools, deployment templates, and continuous training/inference pipelines to eliminate friction. ...

MLOps Engineering Manager £100,000 - £120,000+ DoE Hybrid London 2 x weekly office visits Permanent The Business Join a globally recognised brand with a legacy of excellence and a track record of shaping its industry. While its heritage is well established, the organisation is firmly focused on the future … SageMaker, so you will be working closely with data scientists, engineers and cross-functional stakeholders to deliver this. You will also be responsible for MLOps delivery from predictive maintenance, fault detection, and component lifecycle optimisation, while leading and mentoring a team of engineers. You will also be working with ...

MLOps Engineering Manager £100,000 - £120,000+ DoE Hybrid | London | 2 x weekly office visits Permanent The Business Join a globally recognised brand with a legacy of excellence and a track record of shaping its industry. While its heritage is well established, the organisation is firmly focused on the future … SageMaker, so you will be working closely with data scientists, engineers and cross-functional stakeholders to deliver this. You will also be responsible for MLOps delivery from predictive maintenance, fault detection, and component lifecycle optimisation, while leading and mentoring a team of engineers. You will also be working with ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

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 ...

aware and suitable for advanced analytics and AI use cases. Develop platform capabilities that support AI workloads, including retrieval patterns, feature and vector stores, MLOps, model deployment, monitoring and cost control. Ensure the platform meets security, privacy, regulatory, data governance and responsible-AI expectations. Lead strategic data programmes and platform … observability, reliability, resilience, data quality, security and production support. Practical knowledge of AI, machine learning and Generative AI workloads, including the data, governance, retrieval, MLOps and cost considerations needed to support them. Awareness of responsible AI, data privacy, regulatory obligations and security considerations, particularly in insurance, reinsurance or financial services. ...

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 ...