1 to 25 of 41 MLOps Jobs in Central London

Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits: 💰 Competitive Salary & Bonus : £35,000 – £45,000 plus bonus 🏡 Hybrid Working : A flexible mix of office and remote work 📈 Career Growth : Structured More ❯

scalable systems and high-volume, low latency, transaction/computation applications. Broad understanding of the software development lifecycle, including the application of LLM driven and Data Science DevOps/MLOps/AIOps methodologies. Expertise in data management and analytics, including data structures, data processing, data modelling, and statistical analysis. Knowledge of Python and C# for development and scripting. Leadership: Proven More ❯

leads who are spread across several geographies. The team covers a variety of industries, functions, analytics methodologies and platforms – e.g. Cloud data engineering, advanced statistics, machine learning, predictive analytics, MLOps and generative AI. What You’ll Do You will collaborate closely with a team comprising data scientists, data engineers, product developers, and analytics-focused consultants. As a data savvy Business 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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

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 ❯

direct experience working with YOLO models being a major plus. Experience with large-scale data processing and database technologies such as MongoDB or other NoSQL databases. Practical knowledge of MLOps principles, including CI/CD pipelines using GitHub Actions . Experience with analytics platforms like Google Analytics and business intelligence tools like Tableau or Power BI. Knowledge of GPU-accelerated More ❯

their Databricks practice and needs multiple specialists to join this high-impact initiative. 🎯 DATABRICKS TEAM ROLES: Senior Data Engineering Consultant - Databricks/Snowflake focus Senior ML Engineer - Databricks specialisation (MLOps) Senior Platform Engineer - Databricks deployment expert Senior Databricks ML Engineer - Premium specialist role 🏢 Join a Dedicated Databricks Centre of Excellence: You'll be part of a specialist Databricks team delivering More ❯

engineering, optimisation). Excellent communication and stakeholder management abilities. A track record of managing teams and shaping development roadmaps. experience with cloud environments (AWS, Azure, GCP), and familiarity with MLOps lifecycle or solution architecture. 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 ❯

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 ❯

findings to non-technical stakeholders. Highly desirable skills include: Football Analytics Domain: Significant plus if experienced with football datasets (event, tracking, etc.) and visualization libraries like mplsoccer . Advanced MLOps & Modelling: Deeper experience with the Vertex AI lifecycle (especially Pipelines ) and advanced modelling techniques relevant to football (player valuation, tactical analysis). Bayesian Modelling: Experience with probabilistic programming (e.g., PyMC More ❯

with internal policies and external regulations. Provide technical input on risk mitigation strategies and onboarding documentation. Security & DevSecOps Integration Integrate AI security controls into CI/CD pipelines and MLOps workflows. Use tools such as Azure Key Vault, Microsoft Entra ID, and GitHub Actions for secure deployment and access management. Monitor AI systems using Azure Monitor, Log Analytics, and Application 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. More ❯

in cloud platforms such as Google Cloud. Expertise in statistical techniques such as GLMs, classification, survival models, forecasting, and optimisation. Hands-on experience with experimentation and causal inference. Strong MLOps knowledge and experience deploying models into production environments using CI/CD. Experience with data visualisation tools like Tableau, and communicating to both technical and non-technical audiences. If this More ❯

AI Engineer include: Strong Data Science Foundations – bring solid grounding in statistics and machine learning, along with strong programming skills – Python preferred. Bonus points if you have exposure to MLOps tools and workflows. Passion for Cutting-Edge AI – You actively experiment with and apply modern AI technologies such as LLMs, RAGs, co-pilots, agentic workflows, and more. You’re always More ❯

model optimization (i.e. quantization, pruning) and model deployment frameworks such as TensorRT, ONNX Runtime, and OpenVINO. Proficiency with CUDA programming and optimizing code for GPU acceleration. Strong background in MLOps practices, including CI/CD using GitHub Actions and containerization with Docker. Excellent problem-solving skills and the ability to thrive in a fast-paced, high-intensity environment. Experience contributing More ❯

and deploying object detection models, particularly within the YOLO family. Familiarity with model optimization techniques such as quantization and pruning, and deployment frameworks like TensorRT and OpenVINO. Experience with MLOps tools and practices, including version control (Git), Docker, and CI/CD with GitHub Actions. Excellent problem-solving skills and the ability to perform in a fast-paced, high-intensity More ❯