1 to 25 of 27 MLflow Jobs in the UK

Databricks Unity Catalog and Delta Live Tables. Familiarity with streaming frameworks (Structured Streaming, Kafka, etc.). Background in data observability and metadata management. Exposure to machine learning pipelines or MLflow within Databricks. Knowledge of infrastructure as code (IaC) using Terraform or similar tools. To apply for this role please email across your CV ASAP. More ❯

track record of leading ML engineering projects from architecture to deployment, including ownership of production-grade systems. Deep expertise in ML frameworks and engineering stacks (TensorFlow, PyTorch, JAX, Ray, MLflow, Kubeflow). Proficiency in Python and at least one backend language (e.g., Java, Scala, Go, C++). Strong understanding of cloud ML infrastructure (AWS SageMaker, GCP Vertex AI, Azure ML More ❯

Azure Data Factory), and ensuring data quality for machine learning applications. Machine Learning Operations (MLOps) - Experience deploying ML models to production environments using containerisation (Docker), orchestration (Kubernetes), model versioning (MLflow, DVC), monitoring model performance and drift, A/B testing frameworks, and implementing CI/CD pipelines for automated model training and deployment. Understanding of model governance, explainability, and compliance More ❯

Experience With: Model training, tuning, and deployment in production environments Preferred: Sports data analytics, time-series forecasting, or computer vision experience Infrastructure: AWS/GCP/Azure, Docker, Kubernetes, MLflow, CI/CD pipelines Bonus: Experience with wearable/sensor data, player tracking, or sports video analytics TO BE CONSIDERED Please apply directly by emailing with your CV and availability. More ❯

distributed computing. Familiarity with CI/CD, version control, and DevOps practices. Nice-to-Have Experience with streaming technologies (e.g., Spark Structured Streaming, Event Hub, Kafka). Knowledge of MLflow, Unity Catalog, or advanced Databricks features. Exposure to Terraform or other IaC tools. Experience working in Agile/Scrum environments. To apply for this role please submit your CV or More ❯

distributed computing. Familiarity with CI/CD, version control, and DevOps practices. Nice-to-Have Experience with streaming technologies (e.g., Spark Structured Streaming, Event Hub, Kafka). Knowledge of MLflow, Unity Catalog, or advanced Databricks features. Exposure to Terraform or other IaC tools. Experience working in Agile/Scrum environments. To apply for this role please submit your CV or More ❯

analytics solutions using Databricks in a secure environment. Collaborate with data specialists to deliver efficient, high-quality solutions. Critical Skills Extensive experience with Databricks (including Spark, Delta Lake, and MLflow). Proficiency in ETL/ELT development and orchestration tools (DBT, Airflow, or similar). Hands-on experience with cloud platforms (AWS, Azure, or GCP). Strong knowledge of SQL More ❯

Build APIs and microservices for scalable AI deployment. Use AI-powered dev tools like GitHub Copilot, Cursor, and Codeium to speed up iteration. Apply MLOps/LLMOps practices with MLflow, Weights & Biases, and Kubeflow. ?? Youll Bring Strong Python skills and experience with LangChain, Transformers, Hugging Face. Solid grasp of LLM behavior, prompt optimization, and data engineering. Familiarity with vector databases More ❯

in production environments serving real users. Strong proficiency in containerisation (Docker, Kubernetes) and orchestration of multi-stage ML workflows. Hands-on experience with ML platforms and tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or similar model management systems. Practical knowledge of infrastructure as code, CI/CD best practices, and cloud platforms (AWS, GCP, or Azure). Experience with More ❯

experience in applied data science, machine learning, or analytics leadership. • Strong understanding of model lifecycle management, experimentation frameworks, and data science governance. • Familiarity with MLOps concepts and tooling (e.g., MLflow, Kubeflow, Vertex AI, SageMaker, Azure ML). • Hands-on experience with data science tools and languages such as Python, R, SQL, and relevant frameworks (e.g., scikit-learn, TensorFlow, PyTorch). More ❯

AWS). Knowledge of deep learning, NLP, and computer vision techniques, including experience with Microsoft Copilot or OpenAI APIs. Familiarity with MLOps practices using tools like Azure ML Pipelines, MLflow, Docker, and Kubernetes. Excellent problem-solving and analytical skills. Bachelor's or master's degree in Computer Science, AI, Data Science, or a related field. Additional Details Occasional travel to More ❯

have: Experience deploying machine learning models into production and managing their lifecycle. Experience implementing model governance, including versioning, monitoring, drift detection, and reporting. Familiarity with MLOps tools such as MLflow, Kubeflow, or DVC. Programming skills in Scala. Experience working with ONNX and ONNX Runtime for model optimization and deployment. Experience mentoring or supporting colleagues to help them grow their technical More ❯

and managing scalable AI solutions on at least one major cloud platform (AWS, GCP, Azure). Solid understanding of MLOps principles and experience with relevant tools (e.g., Docker, Kubernetes, MLflow) for building robust, production-ready systems. The Ideal Candidate Profile: This role is a perfect fit if you... Are at the bleeding edge of AI: You don't just follow More ❯

LLMs, and operationalizing models in production. Key Responsibilities: Design, develop, and deploy ML, Deep Learning, and LLM solutions. Implement scalable ML and data pipelines in Databricks (PySpark, Delta Lake, MLflow). Build automated MLOps pipelines with model tracking, CI/CD, and registry. Deploy and operationalize LLMs , including fine-tuning, prompt optimization, and monitoring. Architect secure ML/AI systems … enforce best practices, and lead design/architecture reviews. Required Skills & Experience: 5+ years in ML/AI solution development. Recent hands-on experience with Databricks, PySpark, Delta Lake, MLflow . Experience with LLMs (Hugging Face, LangChain, Azure OpenAI) . Strong MLOps, CI/CD, and model monitoring experience. Proficiency in Python, PyTorch/TensorFlow, FastAPI/Flask . Cloud More ❯

in SQL, Python, and distributed processing (PySpark). Strong communication skills with the ability to translate complex data insights into actionable business strategies. Preferred: Experience in AdTech. Familiarity with MLFlow, Databricks and Azure DevOps. More ❯

comfortable in fast-moving, evolving environments. The tech environment: Python, PyTorch/TensorFlow, HuggingFace Transformers Semantic search/Embeddings/Vector DBs (e.g., FAISS, Pinecone, Weaviate) ML Ops tools (MLflow/Weights & Biases) AWS/Docker/Kubernetes If you’re the Head of AI & Machine Learning we’re looking for then we want to hear from you as soon More ❯

comfortable in fast-moving, evolving environments. The tech environment: Python, PyTorch/TensorFlow, HuggingFace Transformers Semantic search/Embeddings/Vector DBs (e.g., FAISS, Pinecone, Weaviate) ML Ops tools (MLflow/Weights & Biases) AWS/Docker/Kubernetes If you’re the VP of AI & Machine Learning we’re looking for then we want to hear from you as soon More ❯

component of our ML platform. Requirements You Are: Experienced in building and deploying distributed scalable ML pipelines that can process large volumes of energy data daily using Kubernetes and MLflow With solid machine learning engineering fundamentals, fluent in Python, PyTorch, and XGBoost Skilled in developing classification models and anomaly detection systems for production environments Capable of implementing comprehensive data lineage More ❯

You will be designing, developing, and deploying cutting-edge machine learning solutions across the company.If you enjoy end-to-end ownership (from experimentation to deployment), working with AWS, Docker, MLflow, TensorFlow/PyTorch, and contributing to innovative projects with a flexible, results-focused culture, this could be an exciting match. Skills: Ability to write and produce production-grade code in More ❯

Confident teaching ability — whether you've formally taught, mentored or coached technical teams. Mathematical depth — clear understanding of the theory behind ML algorithms. Practical knowledge of tools like FastAPI, MLflow, Docker, and cloud environments (AWS/Azure/GCP). Excellent communication — able to simplify complex technical ideas for learners of mixed ability. Bonus points for: Experience with NLP/ More ❯

Machine Learning Engineer Machine Learning 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 … in. Our client is seeking Machine Learning Engineers with experience in some or all of the following (full training provided to fill any gaps): Python, PyTorch, TensorFlow, Scikit-learn, MLflow, Airflow, Docker, Kubernetes, Pandas, NumPy, SciPy, CI/CD, Data Visualization, Bayesian Modelling, Probabilistic Programming, Terraform, Azure, AWS, GCP, Git, and Agile methodologies. Join a team that's fusing AI More ❯

Machine Learning Engineer (Databricks) - £60 - £70k Edinburgh Hybrid - 2 days onsite Im on the lookout for an MLOps Engineer who can truly bridge the gap between Data Engineering and Data Science. This role is all about leveraging Databricks and Python More ❯

Machine Learning Engineer (Databricks) - £60 - £70k Edinburgh Hybrid - 2 days onsite I’m on the lookout for an MLOps Engineer who can truly bridge the gap between Data Engineering and Data Science. This role is all about leveraging Databricks and More ❯

deployment. Utilizevector databases(Pinecone, Weaviate, Milvus, ChromaDB, pgVector) and embedding stores. UseAI-powered dev tools(GitHub Copilot, Cursor, Codeium, Aider, Windsurf) to accelerate iteration. ImplementLLMOps/PromptOpsusing: Weights & Biases,MLflow,LangSmith,LangFuse,PromptLayer,Humanloop,Helicone,Arize Phoenix Benchmark and evaluate LLM systems usingRagas,DeepEval and structured evaluation suites. Deployment & Infrastructure Containerize and deploy workloads withDocker, Kubernetes, KNative and managed inference …/5, Claude 3.x, Gemini 2.x, Mistral Large, LLaMA 3, Cohere Command R+, DeepSeek Frameworks: LangChain, LlamaIndex, Haystack, AutoGen, CrewAI Tools: GitHub Copilot, Cursor, LangSmith, LangFuse, Weights & Biases, MLflow, Humanloop Cloud: Azure ML, AWS SageMaker, Google Vertex AI, Databricks, Modal Infra: Python, Docker, Kubernetes, SQL/NoSQL, PyTorch, FastAPI, Redis More ❯

heavily on governance, reproducibility, and automation. Real-Time Pipeline Build: Architect and implement solutions to transition high-volume model serving (10M+ customers, 1.2M+ product variants) to real-time performance. MLflow & Databricks Mastery: Lead the optimal integration and use of MLflow for model registry, experiment tracking, and deployment within the Databricks platform. DevOps for ML: Build and automate robust CI/… Engineering team. Key Skills: Must Have: MLOps: Proven experience designing and implementing end-to-end MLOps processes in a production environment. Cloud ML Stack: Expert proficiency with Databricks and MLflow . Big Data/Coding: Expert Apache Spark and Python engineering experience on large datasets. Core Engineering: Strong experience with GIT for version control and building CI/CD/… model fundamentals for optimisation) Familiarity with low-latency data stores (e.g., CosmosDB ). If you have the capability to bring MLOps maturity to a traditional Engineering team using the MLFlow/Databricks/Spark stack, please email: with your CV and contract details. More ❯