51 to 75 of 334 MLOps Jobs in the UK excluding London

Azure/GCP/AWS). Skills we'd also like to hear about: Evidence of modelling experience applied to industry relevant use cases. Familiarity with working in an MLOps environment. Familiarity with simulation techniques. Familiarity with optimisation techniques. What you'll receive from us: No matter where you may be in your career or personal life, our benefits are More ❯

language model training Collaborate with researchers to implement novel data processing pipelines Develop an easy-to-use, secure, and robust developer experience for researchers and engineers Contribute to the MLOps best practices at Lila Sciences and write technical documentation for staff Qualifications: 3+ years of experience in software engineering, with a focus in data engineering or DevOps Demonstrated experience deploying More ❯

managing business-critical machine learning models using Azure ML in Production environments. Experience in data wrangling using Python, SQL and ADF. Experience in CI/CD and DevOps/MLOps and version control. Familiarity with data visualization and reporting tools, ideally PowerBI. Good written and verbal communication and interpersonal skills. Ability to convey technical concepts to non-technical stakeholders. Experience More ❯

strong sense of ownership. A numerate degree (e.g. Maths, Stats, Engineering, Computer Science). Desirable: Experience using Databricks or working in a cloud-based environment like Azure. Exposure to MLOps, version control, or productionising models. Experience working with Jira and Confluence in an Agile environment is advantageous. Streamlit, Power BI or other standard BI software experience Experience from outside the More ❯

and mentoring to grow as a people manager. Opportunity to have a clear and tangible impact across a group of businesses. And you'll be supported by our experienced MLOps and Data Engineering teams, allowing you more time to focus on model development, research and the implementation of new novel algorithmic approaches. Key Responsibilities: Lead the Data Science Operations Team More ❯

Senior Data Engineer An exciting opportunity has arisen for a Senior Data Engineer to join my clients dynamic and growing data team. In this role, you will work across the full data lifecyclestreaming, enrichment, and curationwithin a cloud-based environment. More ❯

At Capgemini Invent, we believe difference drives change. As inventive transformation consultants, we blend our strategic, creative and scientific capabilities, collaborating closely with clients to deliver cutting-edge solutions. Join us to drive transformation tailored to our client's challenges More ❯

cutting-edge machine learning solutions that influence decision-making across a large, fast-moving business. You'll have access to vast datasets, modern tooling, and the support of experienced MLOps and Data Engineering teams – freeing you to focus on model innovation , business impact , and team leadership . Key Responsibilities Lead and coach a team of data scientists focused on pricing … senior stakeholders to prioritise and deliver key initiatives. Work cross-functionally with Marketing and Operations data teams to extend the reach of data science across the organisation. Collaborate with MLOps and Engineering teams to ensure seamless product delivery and integration. Promote the use and value of pricing models to non-technical stakeholders through clear and effective communication. Continuously improve the … flexible hybrid work model (Reading or London) and a collaborative environment. A role where your models directly shape pricing, influence profitability, and deliver real commercial outcomes. Support from seasoned MLOps and engineering teams – letting you focus on research, modelling, and innovation . If you’re passionate about pricing science and ready to step into a leadership role where your work More ❯

to-service communication, API gateways , and data management . AI & Machine Learning Platform: AI is a critical component of the product. You will lead the product vision for our MLOps pipelines and the integration of both traditional and generative AI models into our platform. DevOps & Automation: You will be responsible for the product strategy that underpins engineering enablement. This includes … AWS , and system architecture design. Deep understanding of microservices , Docker , security best practices, DevOps principles, CI/CD , and system monitoring. Direct experience with AI/ML products, including MLOps and the productization of machine learning models (both traditional and generative). Proven ability to bridge business and technical perspectives to make informed trade-offs between speed, quality, and scalability. More ❯

Azure ML Studio and Azure DevOps. Effective Communication: Present findings clearly through visualisation and reporting, including interactive dashboards and web applications using Power BI, R Shiny, or Python Flask. MLOps Maintenance: Maintain and enhance MLOps pipelines, focusing on model robustness and performance. Project Tracking: Manage code and project tracking using Git and Azure DevOps Boards, aligned with Scaled Agile Framework … a candidate with a blend of essential skills and experience, including: Hands-On Experience: Proven experience in data science and machine learning model deployment, preferably with Azure ML Studio. MLOps Proficiency: Strong understanding of pipeline management and model versioning. Data Manipulation Skills: Expertise in data extraction, transformation, and reshaping using SQL, R/Tidyverse, and Python/Pandas. visualisation Expertise More ❯

core AI platform capabilities, supporting a Machine Learning Engineer on classical ML projects, and building the foundation for a future Data Science function. You will also define LLMOps and MLOps practices and ensure the infrastructure is in place to support long-term AI success across the business. Key Responsibilities: Strategy & Leadership Collaborate over the AI roadmap across GenAI, ML, and … lifecycle automation. Maintain awareness of emerging models and integration strategies. Machine Learning Engineering: Support the ML Engineer in model development, deployment, and monitoring. Contribute to pipelines, experimentation design, and MLOps enablement. Provide code and architectural reviews, as well as technical mentoring. Data Science Enablement Act as Data Scientist to support analytics and decision-making needs. Develop experimentation frameworks, metric definitions … and deploying GenAI applications in production. Strong hands-on knowledge of LLMs, prompt engineering, and retrieval-augmented generation (RAG). Practical experience with traditional ML, including data pipelines and MLOps workflows. Working knowledge of statistical modelling and experimentation. Proficiency in Python and at least one additional general-purpose language. Strong understanding of cloud-native architectures (AWS preferred). Experience leading More ❯

desirability scoring) and ensure ethical, bias-free automation. 3. Cross-Functional Leadership - Lead a team of Product Managers (Data, Salesforce, Decision Engine) and collaborate with: - Data Science & Engineering: Scale MLOps, model governance, and data quality. - Commercial Teams: Develop pricing models for data-as-a-service (DaaS) offerings. - Legal & Compliance: Embed GDPR, PSD2, and AI ethics into product design. 4. Commercialisation … Deep technical expertise: - Data Platforms: Cloud-native architectures (AWS/Asure), data mesh/fabric. - Salesforce: Financial Services Cloud, CRM integrations, CPQ. - AI/ML: Production-grade model deployment, MLOps, ethical AI frameworks. - Strong commercial mindset: Experience monetising data or SaaS products. Desired - MBA or advanced degree in Data Science, Computer Science, or related field. - Experience in automotive supply chain More ❯

shipping and maintaining ML models in production, including performance monitoring and optimization. Strong Programming: Proficiency in Python and ML libraries (e.g., PyTorch, scikit-learn, Hugging Face, etc.); familiarity with MLOps tooling and cloud environments (AWS/GCP/Azure). Analytical & Communication Skills: Ability to clearly explain complex ideas, trade-offs, and results to diverse audiences. Nice to Have: Experience More ❯

performance in constrained or sensitive environments Desirable: Prior experience within the defence, aerospace, or national security sectors Familiarity with computer vision, signal processing, or natural language processing Exposure to MLOps, edge computing, or synthetic data generation Knowledge of government or MOD procurement and technical frameworks is an advantage If you are interested in the above position, please contact me, James More ❯

performance in constrained or sensitive environments Desirable: Prior experience within the defence, aerospace, or national security sectors Familiarity with computer vision, signal processing, or natural language processing Exposure to MLOps, edge computing, or synthetic data generation Knowledge of government or MOD procurement and technical frameworks is an advantage If you are interested in the above position, please contact me, James More ❯

investigate failure cases or drift, and recommend iterative improvements Track and report key performance and operational metrics Contribute to best practices in version control, testing, CI/CD, and MLOps Share expertise through peer code reviews, documentation, and collaborative learning initiatives Stay current with emerging trends in AI/ML, particularly in areas such as LLMs, NLP, and intelligent document More ❯

Experience of deployment in a cloud environment Experience with neural networks and Tensor Flow, CatBoost, XGBoost, SKlearn, Pandas Experience with API development SQL experience Software engineering experience DevOps/MLOps experience Good working understanding of CI/CD If you hold the experience and technical skills outlined above which would enable you to hit the ground running, please apply to More ❯

Who are we? Look at the latest headlines and you will see something Ki insures. Think space shuttles, world tours, wind farms, and even footballers’ legs. Ki’s mission is simple. Digitally disrupt and revolutionise a 335-year-old market. More ❯

Founding Machine Learning Engineer - AI Start-Up (On-Site | Equity + Salary) A rare opportunity to join one of London’s most exciting AI start-ups at the ground level! Backed by top-tier investors and led by a world More ❯

At eBay, we're more than a global ecommerce leader - we're changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. We're committed to More ❯

Location: Reading (RG1 1LZ) (2 days p/w in office) Salary: £75k-£85k + bonus Industry: Finance Tech Stack: Python, SQL, Tableau, AWS, Azure 👩🏻‍💻 Great opportunity for a talented Engineer (Python, SQL, Tableau, AWS, Azure) to join a retail More ❯

Location: Cardiff, Wales (CF10 1EP) (2 days p/w in office) Salary: £75k-£85k + bonus Industry: Finance Tech Stack: Python, SQL, Tableau, AWS, Azure 👩🏻‍💻 Great opportunity for a talented Engineer (Python, SQL, Tableau, AWS, Azure) to join a More ❯

Location: Manchester (M2 3JL) (2 days p/w in office) Salary: £75k-£85k + bonus Industry: Finance Tech Stack: Python, SQL, Tableau, AWS, Azure 👩🏻‍💻 Great opportunity for a talented Engineer (Python, SQL, Tableau, AWS, Azure) to join a retail More ❯

and data science solutions for a diverse client base. Role Responsibilities Design and implement ML and Generative AI models to address client challenges Work closely with data engineers and MLOps professionals to deliver production-ready systems Apply techniques such as large language models (LLMs), retrieval-augmented generation (RAG), vector databases, prompt engineering, and model fine-tuning Engage with client stakeholders … Generative AI projects from concept to deployment Hands-on experience with frameworks such as Hugging Face, LangChain, and open-source LLMs Familiarity with tools such as Databricks and modern MLOps workflows Strong Python skills and familiarity with common data science tools and libraries Experience with cloud platforms (preferably Azure, but AWS or GCP also valuable) Confident communicating complex technical ideas More ❯

to production. Build robust APIs and microservices to serve AI models at scale. Integrate behavioural intelligence models across cloud platforms (AWS, GCP, Azure). Set up end-to-end MLOps pipelines: monitoring, retraining, and automation. Collaborate with cross-functional teams to align tech with user-centric product design. What We’re Looking For: 2+ years in AI/ML engineering … backend software roles with ML components. Proficiency in Python and frameworks like PyTorch/TensorFlow, Scikit-learn. Experience deploying models with Docker, Kubernetes, or serverless architectures. Solid grasp of MLOps workflows, versioning, and cloud automation. Strong foundations in algorithms, data structures, and system design. Bonus: Familiarity with behavioural biometrics, sensor-based or time series data An entrepreneurial mindset—curious, autonomous More ❯