12 of 12 MLflow Jobs in the South East

Infrastructure as Code (Terraform), Docker - containerisation. An understanding of kubernetes. Understanding of LLM architectures, inference serving optimization techniques (quantisation, caching), and MLOps tooling (e.g., MLFlow, Kubeflow). Proven experience designing, implementing, and optimising complex RAG pipelines using vector databases (e.g., Pinecone, PGVector). Practical experience with agentic frameworks (e.g., LangChain ...

/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 ...

Computer Science, Data Science, Bioinformatics, Engineering, or a related field, or equivalent experience. Proven experience designing and deploying MLOps pipelines (MLflow, Azure ML, Azure DevOps etc). Strong programming skills in Python and familiarity with common ML/AI libraries (scikit-learn, tensorflow, Keras etc.). Experience implementing machine learning ...

industry experience. Essential Skills Excellent knowledge and experience of managing an on-premise Kubernetes cluster. Excellent knowledge of Kubeflow and similar systems, e.g. MLflow Good programming ability in Python with familiarity with Linux systems including scripting and system configuration. Experience using AWS, e.g, Cognito, S3, EC2, Lamdas, etc. Experience with ...

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 ...

Strong design and development skills using Python. Python's libraries for machine learning: PySpark, Pytorch, scikit-learn and pandas. Technical experience using Databricks and MLFlow (Other Platforms can be considered). Good understanding of software code review standards - CI/CD. Stochastic Simulation modelling experience. If you are interested ...

Machine Learning Engineer Machine Learning Engineer - AI for Advanced Materials - Oxford (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 … 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 ...

LangChain, LangGraph, or custom). Create and maintain data pipelines (Airflow, dbt, Step Functions). Implement monitoring, observability, and automated retraining (CloudWatch, Prometheus, Grafana, MLflow). Embed strong security, compliance, and guardrail practices across all ML workflows. Mentor junior engineers and promote engineering best practices. Key Requirements Essential 3+ years … SageMaker, EKS/Fargate, Lambda, Step Functions, S3). Proficient with Docker, Kubernetes , and IaC (Terraform or AWS CDK). Experience with MLflow , CI/CD tools, and automated testing. Hands-on with LLM deployment and multi-agent systems . Proven experience building data pipelines and production ML workflows. Strong ...

papers, prototyping, and implementing algorithms (AI or otherwise) MLOps: A strong grasp of CI/CD for models, version control, monitoring, and retraining pipelines (MLflow, DVC, Weights & Biases). Cloud: Practical experience deploying AI solutionson AWS Experience with containerisation for deploying and scaling AI solutions (Docker). Software engineering: Fundamental … knowledge of version control (e.g. Git), CI/CD, and testing. Expertise in ML algorithms, model development, and lifecycle tools (MLflow, DVC, Weights & Biases). Able to translate stakeholder needs into innovative AI solutions. Passion for staying current with the AI/ML research landscpape. Why Join Us?Purpose-Driven ...

Azure Machine Learning, Python (pandas, scikit-learn) and Azure SDKs, you will help shape our Data & Productivity capability—designing, training, and deploying models with MLflow/AutoML, and integrating them into real business processes. You’ll collaborate across disciplines (Data Engineering, Security, Product) and work with services such as Azure … data for modelling using Python and Azure-native tools, ensuring quality and consistency across the ML and AI lifecycles.Evaluate, and deploy models using AutoML, MLflow, and custom pipelines, with a focus on performance, scalability, and maint ainability.Monitor model performance, detect drift, and implement retraining strategies to maintain relevance ...

will: Build robust ML and MLOps pipelines for scalable, reproducible model development, deployment, and monitoring. Use tools such as Airflow for workflow orchestration and MLflow for experiment tracking, model registry, and lifecycle management. Work within an agile development environment and help prioritise high-value opportunities for rapid delivery. Essential Skills … skills Hands-on experience developing ML and/or deep learning models for scientific or engineering problems Experience with MLOps tools such as Airflow, MLflow, and containerisation (e.g., Docker) Strong data-visualisation and storytelling skills Interest in materials discovery, computer vision, big data, or optimisation Collaborative communicator, organised, proactive ...

prototyping, and implementing algorithms (AI or oth erwise)MLOps: A strong grasp of CI/CD for models, version control, monitoring, and retraining pipelines (MLflow, DVC, Weights & B iases).Cloud: Practical experience deploying AI solutions on AWSExperience with containerisation for deploying and scaling AI solutions (D ocker).Software engineering: Fundamental … knowledge of version control (e.g. Git), CI/CD, and t esting.Expertise in ML algorithms, model development, and lifecycle tools (MLflow, DVC, Weights & B iases).Able to translate stakeholder needs into innovative AI sol utions.Passion for staying current with the AI/ML research lan dscape. Why Join Us?Purpose ...