126 to 150 of 296 MLOps Jobs in London

Experience: * Python, including APIs, data structures, and async processing * Databricks/Microsoft Fabric * Cloud, preferably Azure (Data Lake, Functions, App Services) * Containerisation with Docker and CI/CD pipelines * MLOps tooling (MLFlow, Git-based versioning, environment tracking) Desirable Skills & Interests * LangChain, Langflow, or similar frameworks for building AI agents * LLMs or intelligent automation workflows * High-availability, scalable systems (microservices, event More ❯

Airflow, Terraform, or SageMaker. Data Quality Management: Experience with data versioning and quality assurance practices. Automation and CI/CD: Knowledge of build and deployment automation processes. Experience within MLOps A 1st class Data degree from one of the UKs top 15 Universities Nice to Have Production Experience: Experience building and maintaining data pipelines in a live environment. Data Storage More ❯

Experience:* Python, including APIs, data structures, and async processing* Databricks/Microsoft Fabric* Cloud, preferably Azure (Data Lake, Functions, App Services)* Containerisation with Docker and CI/CD pipelines* MLOps tooling (MLFlow, Git-based versioning, environment tracking)Desirable Skills & Interests* LangChain, Langflow, or similar frameworks for building AI agents* LLMs or intelligent automation workflows* High-availability, scalable systems (microservices, event More ❯

in data engineering, analytics, or data science. Experience with modern data stacks (e.g., SQL, dbt, Airflow, Snowflake, Looker/Power BI) and AI/ML tooling (e.g., Python, MLflow, MLOps). A track record of building and managing high-performing data teams. Strategic thinking and ability to influence senior stakeholders, comfortable in dual-regulated environments. HOW TO APPLY: Please send More ❯

libraries like Scikit-learn, TensorFlow, PyTorch, or Hugging Face Solid understanding of machine learning fundamentals and performance evaluation techniques Experience working in cloud platforms (AWS, GCP, or Azure) with MLOps tools (e.g. MLflow, SageMaker, Vertex AI) Comfortable working independently and delivering high-quality work to tight timelines Experience working in fast-paced environments or scale-up settings Company Market leading More ❯

deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Conduct rigorous A/B testing and performance measurement to validate impact Act as a technical lead within project teams, mentoring mid-level More ❯

deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Conduct rigorous A/B testing and performance measurement to validate impact Act as a technical lead within project teams, mentoring mid-level More ❯

libraries like Scikit-learn, TensorFlow, PyTorch, or Hugging Face Solid understanding of machine learning fundamentals and performance evaluation techniques Experience working in cloud platforms (AWS, GCP, or Azure) with MLOps tools (e.g. MLflow, SageMaker, Vertex AI) Comfortable working independently and delivering high-quality work to tight timelines Experience working in fast-paced environments or scale-up settings Company Market leading More ❯

with large-scale data and cloud-based environments. Background in consulting or client-facing project delivery. Familiarity with sectors such as manufacturing, energy, healthcare, or public sector. Understanding of MLOps and data science solution lifecycle. Why Join? Work on groundbreaking AI projects with real-world applications.? Collaborate with a team that values innovation, creativity, and measurable impact.? Opportunity to influence 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 Experience in the pensions or similar regulated financial services industry Due to the volume of applications received for More ❯

deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Conduct rigorous A/B testing and performance measurement to validate impact Act as a technical lead within project teams, mentoring mid-level More ❯

deployment of predictive and optimisation models across multiple industries Own the end-to-end ML pipeline: data exploration, feature engineering, modelling, evaluation, and deployment Collaborate with data engineers and MLOps professionals to ensure scalable, production-grade solutions Conduct rigorous A/B testing and performance measurement to validate impact Act as a technical lead within project teams, mentoring mid-level More ❯

systems. Technical Leadership : Proven experience in scaling AI products, DevOps, and cloud architecture. Backend & Data Proficiency : Skilled in Python, Node.js, PostgreSQL, MongoDB, and API security. AI Deployment : Familiarity with MLOps, data engineering, and ethical AI practices. Strategic Thinking : Ability to align tech strategy with business objectives and cost efficiency. Security & Compliance : Strong understanding of GDPR, API authentication, and observability. Big More ❯

systems. Technical Leadership : Proven experience in scaling AI products, DevOps, and cloud architecture. Backend & Data Proficiency : Skilled in Python, Node.js, PostgreSQL, MongoDB, and API security. AI Deployment : Familiarity with MLOps, data engineering, and ethical AI practices. Strategic Thinking : Ability to align tech strategy with business objectives and cost efficiency. Security & Compliance : Strong understanding of GDPR, API authentication, and observability. Big More ❯

knowledge as a plus. Demonstrated experience building APIs supporting analytics, research, or actuarial functions in an insurance environment. Proficiency with cloud technologies such as Snowflake, Databricks, and familiarity with MLOps frameworks. Exhibit strong collaboration, problem-solving, and communication skills, with the ability to adapt to changing priorities and simplify complex problems. Seniority level Seniority level Mid-Senior level Employment type More ❯

experience with molecular or protein visualisation . You've built UIs visualising molecules or proteins using Mol You are passionate about model serving . You have some familiarity with MLOps and ML serving. Your responsibilities Develop and own both front-end and back-end components of our platform. Craft the most graphically delightful web experiences around proteins. Optimise the performance More ❯

out as these open up. What You'll Be Doing As a Senior Machine Learning Engineer at Faculty, you'll design, build, and deploy production-grade software, infrastructure, and MLOps systems that leverage machine learning. You'll be engineering-focused, with a keen interest and working knowledge of operationalised machine learning. You have a desire to take cutting-edge ML More ❯

of language models and transformers Rich understanding of vector stores and search algorithms Large-scale ETL development Direct engineering experience of high performance, large-scale ML systems Hands on MLOps experience, with an appreciation of the end-to-end CI/CD process Have experience supporting fast-paced startup engineering teams A contributor to opensource and always thinking out of More ❯

/implementation/consulting experience of distributed applications - 7+ years management of technical, customer facing resources - 7+ years of design/implementation of production AI/ML systems and MLOps pipelines with hands-on experience with state-of-the-art data and ML frameworks (PyTorch, HuggingFace, Spark, Langchain), technical familiarity with LLMs/FMs, RAG, and prompt engineering techniques. PREFERRED More ❯

practices Track record of leveraging data insights to inform product strategies, ideally with experience in product analytics Knowledge of machine learning frameworks or AI-based products, as well as MLOps best practices(nice to have) Excellent communication and stakeholder management skills, with the ability to concisely report technical information to both technical and non-technical audiences What we offer Diverse More ❯

the buildout of scientific applications and scientific data management applications Building a state-of-the-art platform for conducting ML research with a high velocity and reliability Building key MLOps components for transferring models from research to production at scale Working on low-level performance optimization to increase the efficiency and performance of ML models and infrastructure Optimizing and maximally More ❯

the buildout of scientific applications and scientific data management applications Building a state-of-the-art platform for conducting ML research with a high velocity and reliability Building key MLOps components for transferring models from research to production at scale Working on low-level performance optimization to increase the efficiency and performance of ML models and infrastructure Optimizing and maximally More ❯

Who are we? Our mission is to scale intelligence to serve humanity. We're training and deploying frontier models for developers and enterprises who are building AI systems to power magical experiences like content generation, semantic search, RAG, and agents. More ❯

MLOps Engineer London £60,000-£70,000 About the Role: Join a cutting-edge technology company at the forefront of immersive experiences powered by machine learning and extended reality. We are seeking a skilled MLOps Engineer to help build, deploy, and maintain scalable machine learning infrastructure that drives innovation in our products. You will play a critical role in developing … scalable ML infrastructure that powers immersive XR applications with real-time 3D content. Key Responsibilities: Design, develop, and maintain scalable MLOps pipelines tailored for XR applications, ensuring smooth deployment and monitoring of machine learning models. Collaborate closely with 3D artists and XR developers to integrate machine learning workflows with 3D rendering engines. Optimize ML models and workflows for real-time … related to ML inference and 3D rendering workloads. Develop monitoring solutions to track model performance and system health in production. Stay up to date with the latest trends in MLOps, XR technologies, and 3D rendering techniques to propose innovative improvements. Qualifications: Proven experience as an MLOps engineer or similar role with a focus on 3D applications Strong programming skills in More ❯

best practices to protect data and infrastructure. Please speak to us if you have .. ..the following professional aspirations Deepening Technical Expertise : Aspire to deepen your technical expertise in MLOps practices and master tools and technologies related to cloud platforms, containerisation, and automation. Career Advancement : Aim to progress to a senior MLOps engineer position or potentially transition into a technical … effectively. Mentorship Opportunities : Show interest in mentoring junior engineers and contributing to a collaborative team culture that creates growth and knowledge sharing. Aligning with Business Goals : Aim to align MLOps initiatives with business objectives, ensuring that the ML infrastructure supports the companys strategic direction and contributes to overall success. Exploring Innovative Solutions : Be eager to explore and implement innovative data … data ingestion, storage, and management is essential. Monitoring and Logging Tools : Experience with monitoring and logging tools to track system performance and model effectiveness in production environments. Familiarity with MLOps Tools: Knowledge of various MLOps tools and platforms, including MLflow, Databricks, Kubeflow, and SageMaker, to streamline the machine learning lifecycle. Version Control Systems: Proficient in using version control systems such More ❯