18 of 18 MLflow Jobs in the South East

/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 ...

models through CI/CD pipelines Advanced proficiency in Python (pandas, scikit-learn, PySpark) and SQL. Experience with ML lifecycle tooling such as MLflow and Databricks. Strong understanding of testing, version control, containerisation, and monitoring. Excellent communication skills — able to convey complex ideas clearly to technical and non-technical audiences ...

models through CI/CD pipelines Advanced proficiency in Python (pandas, scikit-learn, PySpark) and SQL. Experience with ML lifecycle tooling such as MLflow and Databricks. Strong understanding of testing, version control, containerisation, and monitoring. Excellent communication skills — able to convey complex ideas clearly to technical and non-technical audiences ...

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 ...

frameworks, and practical experience orchestrating them in enterprise-grade systems. Proficiency in Python, PyTorch, and modern MLOps/AIOps ecosystems (e.g., LangChain, Ray, Kubeflow, MLflow, Hugging Face). Strong foundation in data management, IAM, governance, and security, with experience embedding these principles into AI lifecycle workflows. Expertise in distributed systems ...

scalable ML solutions: Design, train, and deploy machine learning models and workflows with a focus on production-readiness, leveraging tools like Docker Containers, Kubernetes, MLflow, Kafka, and AWS Services. Leverage vector databases and streaming systems: Design and implement solutions with vector databases and Kafka to handle large-scale, high-dimensional … real-time data processing for ML and AI pipelines. Standardize workflows: Use MLflow to manage the end-to-end ML lifecycle, including experiment tracking, model registry, and deployment. Automate and orchestrate: Use orchestration tools like Airflow to manage complex ML workflows and ensure seamless execution at scale. Optimize infrastructure: Design ...

scalable ML solutions: Design, train, and deploy machine learning models and workflows with a focus on production-readiness, leveraging tools like Docker Containers, Kubernetes, MLflow, Kafka, and AWS Services. Leverage vector databases and streaming systems: Design and implement solutions with vector databases and Kafka to handle large-scale, high-dimensional … real-time data processing for ML and AI pipelines. Standardize workflows: Use MLflow to manage the end-to-end ML lifecycle, including experiment tracking, model registry, and deployment. Automate and orchestrate: Use orchestration tools like Airflow to manage complex ML workflows and ensure seamless execution at scale. Optimize infrastructure: Design ...

scalable ML solutions: Design, train, and deploy machine learning models and workflows with a focus on production-readiness, leveraging tools like Docker Containers, Kubernetes, MLflow, Kafka, and AWS Services. Leverage vector databases and streaming systems: Design and implement solutions with vector databases and Kafka to handle large-scale, high-dimensional … real-time data processing for ML and AI pipelines. Standardize workflows: Use MLflow to manage the end-to-end ML lifecycle, including experiment tracking, model registry, and deployment. Automate and orchestrate: Use orchestration tools like Airflow to manage complex ML workflows and ensure seamless execution at scale. Optimize infrastructure: Design ...

scalable ML solutions: Design, train, and deploy machine learning models and workflows with a focus on production-readiness, leveraging tools like Docker Containers, Kubernetes, MLflow, Kafka, and AWS Services. Leverage vector databases and streaming systems: Design and implement solutions with vector databases and Kafka to handle large-scale, high-dimensional … real-time data processing for ML and AI pipelines. Standardize workflows: Use MLflow to manage the end-to-end ML lifecycle, including experiment tracking, model registry, and deployment. Automate and orchestrate: Use orchestration tools like Airflow to manage complex ML workflows and ensure seamless execution at scale. Optimize infrastructure: Design ...

scalable ML solutions: Design, train, and deploy machine learning models and workflows with a focus on production-readiness, leveraging tools like Docker Containers, Kubernetes, MLflow, Kafka, and AWS Services. Leverage vector databases and streaming systems: Design and implement solutions with vector databases and Kafka to handle large-scale, high-dimensional … real-time data processing for ML and AI pipelines. Standardize workflows: Use MLflow to manage the end-to-end ML lifecycle, including experiment tracking, model registry, and deployment. Automate and orchestrate: Use orchestration tools like Airflow to manage complex ML workflows and ensure seamless execution at scale. Optimize infrastructure: Design ...

scalable ML solutions: Design, train, and deploy machine learning models and workflows with a focus on production-readiness, leveraging tools like Docker Containers, Kubernetes, MLflow, Kafka, and AWS Services. Leverage vector databases and streaming systems: Design and implement solutions with vector databases and Kafka to handle large-scale, high-dimensional … real-time data processing for ML and AI pipelines. Standardize workflows: Use MLflow to manage the end-to-end ML lifecycle, including experiment tracking, model registry, and deployment. Automate and orchestrate: Use orchestration tools like Airflow to manage complex ML workflows and ensure seamless execution at scale. Optimize infrastructure: Design ...

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 ...

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 … 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 ...

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 ...

(FEQ127R37) At Databricks, our core values are at the heart of everything we do; creating a culture of proactiveness and a customer-centric mindset guides us to create a unified platform that makes data science ...

P-1127 At Databricks, we are obsessed with enabling data teams to solve the world's toughest problems, from security threat detection to cancer drug development. We do this by building and running the world ...