76 to 100 of 291 MLOps Jobs in London

or cloud platforms like AWS, Lambda, Azure Strong problem-solving skills and critical thinking in AI research Strategic planning abilities Additional Skills (desired): Experience with protein or DNA bioinformatics MLOps expertise Software engineering skills, data pipelining (e.g., Airflow), cloud deployment experience Knowledge of Agile methodologies Interested in joining as our Head of AI? We look forward to hearing from you More ❯

in NLP, unstructured data, and information extraction Eagerness to learn, take feedback, and contribute to a collaborative team Nice to Have: Experience with SQL/NoSQL databases Familiarity with MLOps tools (Docker, Git, CI/CD) Exposure to vector databases or semantic search Knowledge of financial datasets or document processing workflows If you’re excited to grow your skills in More ❯

in NLP, unstructured data, and information extraction Eagerness to learn, take feedback, and contribute to a collaborative team Nice to Have: Experience with SQL/NoSQL databases Familiarity with MLOps tools (Docker, Git, CI/CD) Exposure to vector databases or semantic search Knowledge of financial datasets or document processing workflows If you’re excited to grow your skills in More ❯

and ownership Nice to Have Experience with Data Mesh or Data Lake architectures Familiarity with Kubernetes , Docker, and real-time streaming (Kafka, Kinesis) Exposure to ML engineering pipelines or MLOps Interested? If you're ready to lead a team at the forefront of data innovation in a mission-driven scale-up, apply today or reach out for a confidential chat. More ❯

of complex business cloud solutions. The ideal candidate will have a strong background in Machine Learning engineering and an expert in operationalising models in the Databricks MLFlow environment (chosen MLOps Platform). Responsibilities: Collaborate with Data Scientists and operationalize the model with auditing enabled, ensure the run can be reproduced if needed. Implement Databricks best practices in building and maintaining More ❯

Strong problem-solving skills and ability to think critically about AI research challenges Excellent strategic planning skills Additional Skills (desired but not required): Experience with protein or DNA bioinformatics MLOps experience Software engineering skills and knowledge of data pipelining (e.g. via Airflow) and prior experience of cloud-based ML model deployment Experience with Agile systems Apply for the job Do More ❯

in NLP, unstructured data, and information extraction Eagerness to learn, take feedback, and contribute to a collaborative team Nice to Have: Experience with SQL/NoSQL databases Familiarity with MLOps tools (Docker, Git, CI/CD) Exposure to vector databases or semantic search Knowledge of financial datasets or document processing workflows If youre excited to grow your skills in machine More ❯

supervised and unsupervised learning with an emphasis on vision. Proficiency with deep learning frameworks such as TensorFlow/PyTorch. Proficiency with Python and strong software development background. Experience with MLOps practices, including versioning, deployment, and monitoring of models highly desirable. Ability to communicate complex technical concepts clearly to non-technical stakeholders. Why Work for Proximie? You will be encouraged to More ❯

frameworks (such as Scikit-learn, TensorFlow, PyTorch, Hugging Face) and data manipulation tools (Pandas, NumPy), as well as version control, containerisation, and ML deployment pipelines. Understands how to apply MLOps principles in production environments To be successful in this role you must have hands-on experience designing and deploying ML models in industryideally within agile or cross-functional teams. Understands … to make a difference. Required Knowledge and experience Technical skills: Confident user of MSTeams Strong applied experience in machine learning and/or data science roles Solid understanding of MLOps and production deployment practices Experience with Python and core ML libraries (e.g., Scikit-learn, Pandas, PyTorch, TensorFlow) Familiarity with cloud platforms and data infrastructure (e.g., AWS/GCP/Azure More ❯

client-facing 🎯 Must-haves: 8+ years in Data Science/ML Engineering 2+ years hands-on with Databricks Strong track record delivering production-grade ML models Solid grasp of MLOps best practices Confident speaking to technical and non-technical stakeholders 🛠️ Tech you’ll be using: Python, SQL, Spark, R MLflow, vector databases GitHub/GitLab/Azure DevOps Jira, Confluence More ❯

client-facing 🎯 Must-haves: 8+ years in Data Science/ML Engineering 2+ years hands-on with Databricks Strong track record delivering production-grade ML models Solid grasp of MLOps best practices Confident speaking to technical and non-technical stakeholders 🛠️ Tech you’ll be using: Python, SQL, Spark, R MLflow, vector databases GitHub/GitLab/Azure DevOps Jira, Confluence More ❯

a talented team of researchers to improve and refine algorithms for real-world tasks Ensure the scalability and maintainability of production systems, with a strong focus on (low-level) MLOps What You’ll Be Doing (Machine Learning Researcher): Conduct groundbreaking research to develop new algorithms and models for robotics Work on advanced learning approaches such as reinforcement learning and imitation More ❯

a talented team of researchers to improve and refine algorithms for real-world tasks Ensure the scalability and maintainability of production systems, with a strong focus on (low-level) MLOps What You’ll Be Doing (Machine Learning Researcher): Conduct groundbreaking research to develop new algorithms and models for robotics Work on advanced learning approaches such as reinforcement learning and imitation More ❯

pipelines. Comfortable in fast-paced, agile delivery environments. DESIRABLE SKILLS Experience building or integrating feature stores . Familiarity with Unity Catalog , Databricks SQL , and cost optimisation . Exposure to MLOps practices and production-grade model lifecycle management. Prior experience in financial services or other regulated sectors. HOW TO APPLY Please register your interest by sending your CV via the apply More ❯

Strong communication and stakeholder management skills, with the ability to influence at C-level Background as a hands-on data scientist, with deep understanding of model development, deployment, and MLOps MSc in a quantitative field required; PhD welcome but not essential Familiarity with AI governance and ethical AI practices Curious and up to date on GenAI technologies (either directly or More ❯

scalable AI solutions using cutting-edge tools like LangGraph, FastAPI, and HuggingFace. Own the full AI product lifecycle - from idea to production. Architect intelligent systems using Python, microservices, and MLOps tooling (MLflow, DVC). Work closely with cross-functional teams to turn real-world problems into working software. 🛠️ What You Bring: Fluency in Python and strong grasp of ML/ More ❯

scalable AI solutions using cutting-edge tools like LangGraph, FastAPI, and HuggingFace. Own the full AI product lifecycle - from idea to production. Architect intelligent systems using Python, microservices, and MLOps tooling (MLflow, DVC). Work closely with cross-functional teams to turn real-world problems into working software. 🛠️ What You Bring: Fluency in Python and strong grasp of ML/ More ❯

Strong communication and stakeholder management skills, with the ability to influence at C-level Background as a hands-on data scientist, with deep understanding of model development, deployment, and MLOps MSc in a quantitative field required; PhD welcome but not essential Familiarity with AI governance and ethical AI practices Curious and up to date on GenAI technologies (either directly or More ❯

deployment of high-impact models (personalisation, churn, demand forecasting, pricing, etc.) into production. Establish governance and standards for model quality, explainability, and bias mitigation across jurisdictions. Drive adoption of MLOps best practices - CI/CD, automated testing, monitoring, feature stores. Partner with Marketing, Digital, Technology, Innovation and local business units to embed intelligence into customer and operational workflows. Champion advanced More ❯

deployment of high-impact models (personalisation, churn, demand forecasting, pricing, etc.) into production. Establish governance and standards for model quality, explainability, and bias mitigation across jurisdictions. Drive adoption of MLOps best practices - CI/CD, automated testing, monitoring, feature stores. Partner with Marketing, Digital, Technology, Innovation and local business units to embed intelligence into customer and operational workflows. Champion advanced More ❯

/sub patterns for asynchronous processing Integrate with third-party data sources like Snowflake and Databricks Create robust error handling, monitoring, and retry systems for high availability Collaborate with MLOps and DevOps teams to optimise architecture and deployment pipelines What You'll Bring 5+ years in backend or fullstack engineering roles Strong Python skills, ideally with FastAPI and the ASGI More ❯

Language Processing/Generative AI/Image Recognition Extensive experience with machine learning techniques and algorithms such as supervised and unsupervised learning techniques, predictive modelling and statistics. Experience with MLOps Excellent organisation skills, working independently and ability to deliver results for deadlines. A proactive, innovative, pragmatic approach to problem-solving and an ability to think critically and objectively. Good customer More ❯

the Solutions Architect role at JFrog Join to apply for the Solutions Architect role at JFrog Residency in London Area is required At JFrog, were reinventing DevOps, DevSecOps and MLOps to help the worlds greatest companies innovate, develop faster and be more secure -- and we want you along for the ride. This is a special place with a unique combination More ❯

integrations (e.g., Hyperscaler SDKs to Salesforce Flows). Prior exposure to conversational voice pipelines or multimodal integrations via hyperscaler services. Advanced AI/ML: Exposure to frameworks (TensorFlow, PyTorch), MLOps practices, and cloud AI platforms (e.g., Google Vertex AI, AWS Sagemaker). Hands-on work with Generative AI, Large Language Models (LLMs), agent-based frameworks, and prompt engineering. *LI-Y More ❯

rest/in-transit), token management Identity integration (Google Identity, Azure Entra ID) Data Security & Governance RAG pipelines with data classification, masking, and DLP GDPR and data residency compliance MLOps & DevSecOps GitHub Actions, CI/CD security testing, model drift detection, audit logging Lab Environment Tooling Infrastructure-as-Code (IaC): Terraform, Pulumi Containerization & Orchestration: Docker, Kubernetes (GKE/AKS) Networking More ❯