176 to 200 of 698 MLOps Jobs in the UK

infrastructure automation. Hands-on work with Docker containers, security-focused architecture, and multi-provider integrations (cloud compute + frontier model providers) Familiarity with MLOps, model onboarding, compute orchestration, and infrastructure for large-scale AI usage Proficiency in Python and experience with additional languages Knowledge of Kubernetes, container orchestration, observability ...

OECD AI Principles, model risk management, or responsible AI frameworks. Experience with AI red teaming, model evaluation, bias testing, explainability tooling, monitoring tools, MLOps, LLMOps, or AI governance platforms. Experience advising on third-party AI tools, enterprise AI platforms, or AI-enabled vendor solutions. Certifications or advanced training ...

mentoring others or providing technical leadership. Awareness of GDPR and international data regulations. Useful, but not essential Causal inference and large-scale experiment design. MLOps, including model registries, artefact versioning and drift monitoring. Streamlit or similar tools for internal applications and dashboards. Experience with claims, refunds or customer operations. ...

fits into a drug‐discovery or materials‐design workflow, plus familiarity with cheminformatics tooling (e.g. RDKit, OpenEye) — or willingness to pick these up. MLOps fluency: experiment tracking, data versioning, model serving, and observability of deployed models. A visible track record in the field — peer‐reviewed publications, open‐source contributions ...

aligned to client objectives. Work with engineering, architecture and product teams to take prototypes through to production, monitoring and scale using appropriate cloud and MLOps practices. Lead the day‐to‐day technical delivery of a workstream, managing quality, risks and dependencies while coaching and developing team members. Communicate analytical findings ...

Build and operate ML services and APIs with authentication, role-based access and tenant isolation, so client data is never mixed across tenants. Own MLOps in production: experiment tracking, model and prompt versioning, monitoring, drift detection, cost and latency management, and incident response. Write unit, integration and evaluation tests ...

problem‐solver at heart. Nice to Haves (but we can teach you): You've already worked with Rust in a production environment. Experience with MLOps, particularly the challenges of managing the lifecycle of models in a multi‐tenant, high‐availability system. Familiarity with building ML inference services, model serialization (e.g. ...

cost management Model serving and inference at production scale (eg KServe, RayServe, Triton, vLLM, or similar) with real latency and cost constraints(preferred RayServe) MLOps pipeline tooling - training pipelines, model registries, feature stores, and lineage (Kubeflow, MLflow, Feast, Weights & Biases, or equivalents) LLMOps in production - inference serving, prompt/version ...

delivery at scale. Excellent communication and stakeholder-management skills, with fluent English (written and spoken). Preferred Experience and/or Qualifications Familiarity with MLOps or LLMOps practices, and with monitoring and observing the behaviour of AI systems in production. Exposure to prompt design and prompt/response evaluation techniques ...

aligned to client objectives. Work with engineering, architecture and product teams to take prototypes through to production, monitoring and scale using appropriate cloud and MLOps practices. Lead the day-to-day technical delivery of a workstream, managing quality, risks and dependencies while coaching and developing team members. Communicate analytical findings ...

fraud, or abuse detection Proficiency in Python and experience writing production-quality code, with a solid understanding of data pipelines, model training workflows, and MLOps practices Demonstrated ability to own problems end-to-end, operate in ambiguous environments, and make pragmatic technical decisions Strong collaboration and communication skills, with ...

technical designs throughout the project lifecycle. Experience with large-scale data initiatives, data labelling strategies, synthetic data generation and data quality management. Familiarity with MLOps practices and cloud platforms for AI deployment. Experience working with academic partners, research institutions, grant-funded programmes or collaborative research consortia. Experience contributing to bids ...

compliance laws. Platform financial acumen - demonstrable experience of optimising SaaS platform spend, and the use of forecasting models. Bonus Skills AI/ML Engineering & MLOps, including model deployment/serving and monitoring. Experience with federated learning and/or privacy‐enhancing technologies (PETs). Direct experience with LLM Ops, including ...

cloud data warehousing principles. Expertise with cloud data warehouse solutions such as Databrick, Snowflake or Redshift. Proven experience in DataOps, CI/CD (MLOps is a plus) Exceptional written, verbal, and listening communication skills, with the ability to effectively explain complex technical concepts to both technical and non-technical audiences. ...

CreateFuture seeks a Senior Data Engineer with an ML focus to own data and feature pipelines for production ML systems. You will collaborate with data scientists and ML engineers, mentor junior engineers, and elevate engineering ...

CreateFuture seeks a Senior Data Engineer with an ML focus to own data and feature pipelines for production ML systems. You will collaborate with data scientists and ML engineers, mentor junior engineers, and elevate engineering ...

CreateFuture seeks a Senior Data Engineer with an ML focus to own data and feature pipelines for production ML systems. You will collaborate with data scientists and ML engineers, mentor junior engineers, and elevate engineering ...

CreateFuture seeks a Senior Data Engineer with an ML focus to own data and feature pipelines for production ML systems. You will collaborate with data scientists and ML engineers, mentor junior engineers, and elevate engineering ...

Understanding Recruitment Ltd is seeking a Machine Learning Engineer to own end-to-end production AI systems and cloud infrastructure. You will ship and operate AI/LLM services in a production environment, with emphasis ...

BAE Systems Digital Intelligence in London is seeking a Senior ML Engineer to design, optimize and productionize machine learning and GenAI solutions for national security problems. You will collaborate with Data Scientists, Software Engineers, Product ...

Hiscox is seeking an experienced Machine Learning Engineer to join a newly formed ML Engineering team. You will build and maintain infrastructure to deploy data‐driven models, and develop Python APIs to serve ML models ...

projects, prototype GenAI, RAG, and predictive models, and build scalable pipelines with data engineers and IT teams aligned to CI/CD and MLOps standards. The role requires an advanced degree and deep Python/SQL experience, plus the ability to communicate complex analytics #J-18808-Ljbffr ...

with multimodal models (vision + language) Experience with neural rendering or 3D generation Open-source contributions or research publications Real-time inference optimization Strong MLOps understanding Ability to build end-to-end ML-driven features #J-18808-Ljbffr ...

integrations Fine‐tune, optimize, and deploy large language and multi‐modal models with a focus on scalability, reliability, and low‐latency performance Build scalable MLOps pipelines and cloud‐native AI applications to support secure, production‐grade deployments Collaborate with clients and cross‐functional teams to shape AI engineering strategy ...

Performance Analysis with AI Manage the end-to-end lifecycle of trades across multi-asset classes. Develop agentic AI workflows using Python (ML) and MLOps tools to automate manual operations and reconciliations Perform daily P&L attribution and explain variance by decomposing market moves, Greeks, and new activity. Leverage ...