1 to 25 of 100 Permanent MLOps Jobs in the UK excluding London

or experimental Jupyter notebooks to full production deployment. Strong programming skills in Python, with familiarity in ML libraries/frameworks such as TensorFlow, PyTorch, and Scikit-learn. Experience with MLOps practices including model drift detection, decay, A/B testing, integration testing, differential testing, Python package building, and code version control. Skilled in data pipeline creation and working with both More ❯

SQL, NoSQL, and big data technologies (Spark, Hadoop, Snowflake, Databricks, etc.). Strong background in statistical modelling, probability theory, and mathematical optimization. Experience deploying machine learning models to production (MLOps, Docker, Kubernetes, etc.). Familiarity with AWS/GCP/Azure cloud ML platforms for scalable model training and inference. Strong problem-solving, communication, and business acumen skills. Experience in More ❯

familiar with process management tools such as JIRA, Target process, Trello or similar Nice to Have: ● Familiarity with data security, privacy, and compliance frameworks ● Exposure to machine learning pipelines, MLOps, or AI-driven data products ● Experience with big data platforms and technologies such as EMR, Databricks, Kafka, Spark ● Exposure to AI/ML concepts and collaboration with data science or More ❯

familiar with process management tools such as JIRA, Target process, Trello or similar Nice to Have: ● Familiarity with data security, privacy, and compliance frameworks ● Exposure to machine learning pipelines, MLOps, or AI-driven data products ● Experience with big data platforms and technologies such as EMR, Databricks, Kafka, Spark ● Exposure to AI/ML concepts and collaboration with data science or More ❯

principles, and testing practices. Knowledge and working experience of AGILE methodologies. Proficient with SQL. Familiarity with Databricks, Spark, geospatial data/modelling and insurance are a plus. Exposure to MLOps, model monitoring principles, CI/CD and associated tech, e.g., Docker, MLflow, k8s, FastAPI etc are desirable We’ll help you gain Experience working in a high-performance environment where More ❯

to strengthen our Systematic Research team, we are looking for a London based: Quantitative Analyst We are seeking a hands on quantitative researcher with strong software/DevOps/MLOps capability to develop, productionise, and scale systematic investment models and sustainability analytics. You will own the end to end research to production lifecycle : automating data operations, building and validating quant More ❯

and ability to communicate complex ideas clearly. Desirable Skills Experience with deep learning architectures (CNNs, RNNs, Transformers). Familiarity with cloud platforms (AWS, GCP, or Azure). Exposure to MLOps tools or pipeline automation. Previous internship or research experience in a related field. What’s on Offer Competitive graduate salary and benefits package. A collaborative, learning-focused environment. Opportunity to More ❯

LLM orchestration frameworks, and prompt tuning. Familiarity with RAG pipelines, embedding models, and vector databases. Experience with cloud platforms (AWS, GCP, Azure) and AI/ML services. Knowledge of MLOps tools and practices (e.g., MLflow, Kubeflow, Vertex AI, Azure ML). Strong understanding of data engineering, data pipelines, and ETL workflows. Excellent problem-solving, communication, and stakeholder engagement skills. Bachelor More ❯

real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from SQL to large-scale distributed data tools (Spark, etc.). Strong written and verbal communication skills, especially More ❯

for predominantly time-series forecasting Collaborate with data scientists and researchers to productionise models Manage cloud-based and on-prem setup Requirements: 25 years experience in ML engineering/MLOps roles Strong proficiency in Python and experience with relevant ML libraries/frameworks (pandas, numpy, sklearn, TensorFlow, PyTorch) Strong understanding and experience in implementing end-to-end ML pipelines (data More ❯

Engineer Machine Learning Engineer – AI for Advanced Materials – Oxford (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We’re looking for a Machine Learning Engineer to join a rapidly scaling deep-tech company that’s reinventing how the world designs More ❯

Engineer - AI for Advanced Materials - Oxford/Remote (UK) (Tech stack: Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, AWS, Azure, GCP, Pandas, NumPy, SciPy, CI/CD, MLOps, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform) We're looking for a Machine Learning Engineer to join a rapidly scaling deep-tech company that's reinventing how the world designs More ❯

with Front end Frameworks such as Angular, for integration of AI-powered applications Experience with graph databases and knowledge graphs (Neo4j) for knowledge graphs and tool routing. Cloud deployment & MLOps Production deployments on Azure (AKS/ACI/Functions), CI/CD, and Infrastructure as Code (Bicep/Terraform). Data & Information Management Experience with relational/semi-structured database More ❯

with cloud platforms like Microsoft Azure or Google Cloud Platform (GCP); familiarity with Databricks is a plus. Comfort working in cloud-based or secure environments, with an interest in MLOps or model deployment. Awareness and practice of responsible AI, data ethics, and governance frameworks. Willingness to explore emerging technologies such as agentic AI, GitHub Copilot, Microsoft Copilot and Copilot Studio. More ❯

Job Title: MLOps Engineer Location: Bristol – 1-day onsite p/m Start Date: ASAP Salary: £75,000 - £90,000 Note: Candidates must be willing and eligible to undergo SC Clearance. Join a European leader in AI-driven technology, delivering innovative surveillance solutions that enhance safety and save lives. They provide cutting-edge services combining real-time intelligence, data, and … AI to transform operational outcomes globally. About the Role We are seeking an MLOps Engineer to manage and optimise the machine learning lifecycle, from development to deployment and monitoring. You will work closely with data scientists, software engineers, and IT teams to ensure seamless, scalable, and reliable machine learning solutions in production environments. Key Responsibilities - Pipeline Development: Build and maintain … deliver solutions that meet technical and business requirements. - Security & Governance: Implement best practices for data security, model governance, and compliance. - Documentation & Innovation: Maintain clear documentation and stay updated on MLOps trends and technologies. Profile & Requirements - Bachelor’s or Master’s in Computer Science, Data Science, Engineering, or related field. - 3+ years in ML, DevOps, or MLOps with hands-on experience More ❯

R), SQL, and experience with notebooks, Git workflows, and Power BI. Working knowledge of supervised machine learning (e.g., gradient boosting, logistic regression), evaluation metrics, and experiment design. Exposure to MLOps concepts, cloud platforms (e.g., Azure), and GenAI tools is a strong plus. Structured thinking, strong problem-solving, and clear communication skills. Degree (2:1 or equivalent) in a numerical discipline More ❯

products Ensure best practices in data engineering and contribute to architectural decisions Support senior team members in identifying and addressing data science opportunities. Required Skills & Experience Proven experience in MLOps or DevOps roles within machine learning environments Strong programming skills in Python, with hands-on experience in PySpark and SQL Deep understanding of ML lifecycle management and CI/CD More ❯

pragmatic mindset - able to balance innovation with execution. Nice-to-haves: Experience leading platform or DevOps product teams in life sciences, research, or AI-driven organisations . Familiarity with MLOps, GenAI, and large-scale compute orchestration . Background in computer science, engineering, or a related technical discipline. Package & Benefits: Base salary up to £145,000 . Car allowance , 18% bonus More ❯

R), SQL, and experience with notebooks, Git workflows, and Power BI. Working knowledge of supervised machine learning (e.g., gradient boosting, logistic regression), evaluation metrics, and experiment design. Exposure to MLOps concepts, cloud platforms (e.g., Azure), and GenAI tools is a strong plus. Structured thinking, strong problem-solving, and clear communication skills. Degree (2:1 or equivalent) in a numerical discipline More ❯

marketing performance managers. Experience in Python and SQL. Experience with cloud platforms such as Amazon Web Services, Azure, or Google Cloud Platform. Deep understanding of Generative AI, NLP, and MLOps concepts. Analytical, flexible, independent, and a strong communicator. WE OFFER: A flexible, hybrid working policy (2 days from the office, depending on location). An excellent salary based on experience More ❯

for security clearance. Desirable Skills, but not essential: Proficiency in Arcade scripting across ESRI runtime environments. Familiarity with the Microsoft Azure cloud platform, including data and AI services, and MLOps Knowledge of geospatial data warehousing and analytics. Familiarity with leading AI/ML platforms and libraries Experience building and scheduling ETL workflows. Knowledge of spatial data management and geospatial queries. More ❯

Factory, GCP Dataflow and AWS services. Apply data modelling concepts (e.g., medallion architecture) and ensure data quality and governance. Collaborate with DevOps teams to automate CI/CD and MLOps processes using Azure DevOps, Kubernetes, and Terraform. Visualize and communicate insights using Power BI, Tableau, and PowerApps. Mentor junior engineers and contribute to internal knowledge sharing. Ensure solutions meet security More ❯

that drives engagement, conversion, and growth. Oversee the design, training, and deployment of search and recommendation models — from data strategy to monitoring and performance optimisation. Collaborate with platform and MLOps teams to ensure robust, efficient, and scalable ML workflows (including CI/CD, feature management, and monitoring). Share insights and best practices across other ML teams, particularly in areas More ❯

that drives engagement, conversion, and growth. Oversee the design, training, and deployment of search and recommendation models — from data strategy to monitoring and performance optimisation. Collaborate with platform and MLOps teams to ensure robust, efficient, and scalable ML workflows (including CI/CD, feature management, and monitoring). Share insights and best practices across other ML teams, particularly in areas More ❯

that drives engagement, conversion, and growth. Oversee the design, training, and deployment of search and recommendation models — from data strategy to monitoring and performance optimisation. Collaborate with platform and MLOps teams to ensure robust, efficient, and scalable ML workflows (including CI/CD, feature management, and monitoring). Share insights and best practices across other ML teams, particularly in areas More ❯