76 to 100 of 141 MLOps Jobs in the UK

maintain secure AWS cloud infrastructure Implement and manage CI/CD and DevOps pipelines Champion data quality, security, and best practices Collaborate with cross-functional teams Implement and manage MLOps capabilities Essential Skills: Advanced Python programming skills Expertise in data engineering tools and frameworks (Apache Flink) Hands-on AWS experience (Serverless, CloudFormation, CDK) Strong understanding of containerization, CI/CD More ❯

reinforcement learning Model selection, training, validation, and evaluation Deep learning architectures (CNNs, RNNs, Transformers) Data Science Statistical analysis, feature engineering, A/B testing Experiment design and hypothesis testing MLOps & Engineering Scalable ML systems (batch and real-time) ML pipelines, CI/CD, monitoring, deployment Familiarity with tools like MLflow, Kubeflow, Airflow, Docker, Kubernetes Strategic skills Align ML initiatives with More ❯

ML architecture and system design (minimum 2 years) with proven expertise in hands-on coding AI solutions. Deep knowledge of machine learning frameworks, cloud platforms (AWS, Azure, GCP), and MLOps(machine learning operations) practices. Deep knowledge about design and implementation of secure integrations between AI solutions and legacy enterprise software, whilst ensuring compliance with government-level security requirements, implementing single More ❯

ML architecture and system design (minimum 2 years) with proven expertise in hands-on coding AI solutions Deep knowledge of machine learning frameworks, cloud platforms (AWS, Azure, GCP), and MLOps(machine learning operations) practices. Deep knowledge about costing of AI models to produce reasonable ROI and modular reusable services with moderate level of maintenance Understanding of data architecture, API design More ❯

ML architecture and system design (minimum 2 years) with proven expertise in hands-on coding AI solutions. Deep knowledge of machine learning frameworks, cloud platforms (AWS, Azure, GCP), and MLOps(machine learning operations) practices. Deep knowledge about design and implementation of secure integrations between AI solutions and legacy enterprise software, whilst ensuring compliance with government-level security requirements, implementing single More ❯

ML architecture and system design (minimum 2 years) with proven expertise in hands-on coding AI solutions Deep knowledge of machine learning frameworks, cloud platforms (AWS, Azure, GCP), and MLOps(machine learning operations) practices. Deep knowledge about costing of AI models to produce reasonable ROI and modular reusable services with moderate level of maintenance Understanding of data architecture, API design More ❯

these AI products. You will be building tools for model training and evaluation, collaborating with Data Scientists and Engineers to get these solutions into production, and driving improvements in MLOps processes. You'll have worked as a Machine Learning Engineer or Data Scientist in the past, ideally centred around a software product, and have solid Python coding skills, and expertise More ❯

development, ideally in regulated industries. Sector Knowledge: Financial services, consulting, or technology background with a focus on transformation. Technical Understanding: Familiarity with AI/ML technologies (generative AI, NLP, MLOps, data governance). Business Acumen: Ability to identify high-impact AI use cases aligned to business strategy. Governance Awareness: Strong understanding of risk, compliance, and model governance in a complex More ❯

deploying AI applications using Azure AI tools. Hands-on experience with databases such as Cosmos DB and SQL Server. Knowledge of Azure cloud services and deployment pipelines (DevOps/MLOps). Strong coding skills in Python and experience with REST APIs. Experience ensuring compliance with data protection, cybersecurity, and ethical AI standards Desirable Qualifications: Microsoft Azure AI Engineer Associate or More ❯

Senior Data & AI Scientist page is loaded Senior Data & AI Scientistlocations: Bristoltime type: Full timeposted on: Posted Todaytime left to apply: End Date: October 24, 2025 (30 days left to apply)job requisition id: 139255 End Date Thursday 23 October More ❯

haves Python wizardry and strong PyTorch . Hands-on experience with Audio/Video AI - HuggingFace Transformers, SpeechBrain or torchaudio, one detection/pose stack (YOLO, MediaPipe). Production MLOps: experiment tracking, model registries, CI/CD, model monitoring. Comfortable with GCP, containers, and GPUs. Located in London or ready to relocate . Nice to haves Real-time inference experience More ❯

and scaling cross-functional teams (Data Science, ML Engineering, Software Engineering, Managers). Defining the ML/AI roadmap and aligning it to business outcomes. Driving best practice in MLOps, deployment, observability, and automation. Working with cutting-edge approaches: deep learning frameworks, foundation models, knowledge graphs, etc. Being the voice of ML/AI leadership, setting standards, mentoring, and building More ❯

or prototypes at high velocity, ideally in startup or research contexts where speed and adaptability are paramount. Deep expertise in Generative AI, multi-agent systems, LLMs, end-to-end MLOps, and AI infrastructure. Exposure to Deep Learning, Reinforcement Learning, federated learning, AI evaluation or ML fundamentals is highly beneficial. Active interest and awareness of SOTA in multi-agent systems, collective More ❯

of working with cloud services, and the main challenges involved Understanding and experience of experimentation within data science, e.g. A/B testing Experience with model lifecycle management and MLOps Experience mentoring other team members Additional Information If you can bring some of these skills and experience, along with transferable strengths, we'd love to hear from you and encourage More ❯

as a C# Developer with proficiency in Python, .NET framework and C# as well as experience with ML.NET, ASP.NET, MVC, Web API Containers (Docker/Kubernetes), Machine Learning Operations (MLOps) and CI/CD Good knowledge of IT industry trends (especially GenAI and AgenticAI), suppliers, platforms and products Post productions MLOps, Model retaining and Drift monitoring in Azure with knowledge More ❯

initiatives. Working for a US Sports analytics client you'll collaborate with their technical team, providing advanced mathematical models derived from large datasets and statistics for the client's MLOps team to productionise. Location/WFH: You'll join a friendly and sociable team in the London office two days a week (Mondays and Thursdays) with flexibility to work from More ❯

initiatives. Working for a US Sports analytics client you'll collaborate with their technical team, providing advanced mathematical models derived from large datasets and statistics for the client's MLOps team to productionise. Location/WFH: You'll join a friendly and sociable team in the London office two days a week (Mondays and Thursdays) with flexibility to work from More ❯

initiatives. Working for a US Sports analytics client you'll collaborate with their technical team, providing advanced mathematical models derived from large datasets and statistics for the client's MLOps team to productionise. Location/WFH: You'll join a friendly and sociable team in the London office two days a week (Mondays and Thursdays) with flexibility to work from More ❯

will use your skills to: 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 enable sustained improvements to products … Head of Data Science, and Head 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. Tune machine learning methods for … of the following predictive modelling techniques; Logistic Regression, GBMs, Elastic Net GLMs, GAMs, Decision Trees, Random Forests, Neural Nets and Clustering Experience in DevOps and Azure ML, or other MLOps and ML Lifecycle technology stacks, such as AWS, Databricks, Google Cloud, etc. Experience with deploying services in Docker and Kubernetes Experience in creating production grade coding and SOLID programming principles More ❯

large (AI) transformational journeys BCG does for its clients. Often involves the following engineering disciplines : Cloud Engineering Data Engineering (not building pipelines but designing and building the framework) DevOps MLOps/LLMOps Often work with the following technologies : Azure, AWS, GCP Airflow, dbt, Databricks, Snowflake, etc. GitHub, Azure DevOps and related developer tooling and CI/CD platforms, Terraform or … other Infra-as-Code MLflow, AzureML or similar for MLOps; LangSmith, Langfuse and similar for LLMOps The difference to our "AI Engineer" role is: Do you "use/consume" these technologies, or are you the one that "provides" them to the rest of the organization. What You'll Bring TECHNOLOGIES: Programming Languages: Python Experience with additional programming languages is a More ❯

Job Title: ML Ops/LLM Ops Engineer Location: Hybrid (potentially 1 day per week in east London) Contract: 6 month Contract Day Rate: £400 per day (Outside IR35) About the Role We are seeking an experienced ML Ops/ More ❯

you will be the bridge between rapid AI development and the stringent security and compliance requirements of the financial industry. You will design and implement robust, scalable, and secure MLOps platforms that enable our data scientists to innovate safely and at speed, ensuring the integrity, confidentiality, and availability of our models and data. Key Responsibilities Secure MLOps Platform Engineering: Design … implement, and manage secure, automated CI/CD pipelines specifically for machine learning models (MLOps), integrating security checks (SAST, DAST, SCA) and data validation gates. AI/ML Infrastructure Security: Harden and secure the underlying cloud infrastructure for AI/ML workloads, including GPU clusters, container orchestration (Kubernetes), and managed services (e.g., AWS SageMaker, Azure ML). Security by Design … feature store, model training, deployment, monitoring). Implement secrets management, network security (firewalls, VPCs), and identity and access management (IAM) for data and model assets. Compliance & Governance: Ensure the MLOps platform adheres to stringent financial industry regulations (e.g., GDPR, SOX, PCI-DSS, SWIFT CSCF) and internal policies (Model Risk Management). Automate compliance evidence collection. Threat Modeling & Risk Assessment: Proactively More ❯

initiatives. Working for a US Sports analytics client you'll collaborate with their technical team, providing advanced mathematical models derived from large datasets and statistics for the client's MLOps team to productionise. Location/WFH: You'll join a friendly and sociable team in the London office two days a week (Mondays and Thursdays) with flexibility to work from More ❯

initiatives. Working for a US Sports analytics client you'll collaborate with their technical team, providing advanced mathematical models derived from large datasets and statistics for the client's MLOps team to productionise. Location/WFH: You'll join a friendly and sociable team in the London office two days a week (Mondays and Thursdays) with flexibility to work from More ❯

cloud data. Build end-to-end data pipelines : collect sim/tele-op logs, manage versioned data lakes, apply weak supervision, curate datasets, and run evaluation loops. Collaborate with MLOps and Data Platform teams to scale distributed training and optimise models for real-time deployment . Hire, mentor, and lead a high-calibre team of research scientists and engineers. What More ❯