25 of 25 Permanent MLflow Jobs in the Midlands

experience developing, evaluating and orchestrating agentic workflows MLOps and Distributed Systems: Hands-on experience with distributed systems and MLOps tooling such as Kubernetes, Docker, MLflow, Airflow, Terraform, and CI/CD. Preferred Skills Data Streaming and Orchestration: Familiarity with tools such as Kafka, Flink, Spark, dbt, and Airflow. Multi-Modal ...

experience developing, evaluating and orchestrating agentic workflows MLOps and Distributed Systems: Hands-on experience with distributed systems and MLOps tooling such as Kubernetes, Docker, MLflow, Airflow, Terraform, and CI/CD. Preferred Skills Data Streaming and Orchestration: Familiarity with tools such as Kafka, Flink, Spark, dbt, and Airflow. Multi-Modal ...

experience developing, evaluating and orchestrating agentic workflows MLOps and Distributed Systems: Hands-on experience with distributed systems and MLOps tooling such as Kubernetes, Docker, MLflow, Airflow, Terraform, and CI/CD. Preferred Skills Data Streaming and Orchestration: Familiarity with tools such as Kafka, Flink, Spark, dbt, and Airflow. Multi-Modal ...

computer vision techniques, especially in the context of Microsoft Copilot and 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 ...

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. About Us You know Next, but did you know we’re a FTSE-100 retail company employing over 35,000 people ...

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 ...

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 ...

brand estate with high stakeholder visibility Coach teams, guide decisions, and bring structure to an ambiguous environment Key Skills AWS SageMaker (essential) Snowflake GitLab MLflow AWS Bedrock (bonus) Soft Skills Thrives in ambiguity Able to navigate political landscapes and conflicting priorities Strong communicator with the ability to explain ML concepts ...

brand estate with high stakeholder visibility Coach teams, guide decisions, and bring structure to an ambiguous environment Key Skills AWS SageMaker (essential) Snowflake GitLab MLflow AWS Bedrock (bonus) Soft Skills Thrives in ambiguity Able to navigate political landscapes and conflicting priorities Strong communicator with the ability to explain ML concepts ...

brand estate with high stakeholder visibility Coach teams, guide decisions, and bring structure to an ambiguous environment Key Skills AWS SageMaker (essential) Snowflake GitLab MLflow AWS Bedrock (bonus) Soft Skills Thrives in ambiguity Able to navigate political landscapes and conflicting priorities Strong communicator with the ability to explain ML concepts ...

brand estate with high stakeholder visibility Coach teams, guide decisions, and bring structure to an ambiguous environment Key Skills AWS SageMaker (essential) Snowflake GitLab MLflow AWS Bedrock (bonus) Soft Skills Thrives in ambiguity Able to navigate political landscapes and conflicting priorities Strong communicator with the ability to explain ML concepts ...

platform Building robust infrastructure and CI/CD pipelines End-to-end orchestration of ML workflows Working across AWS (SageMaker), Snowflake, GitLab, and MLflow Contributing to platform design, best practices, and production readiness (Bonus) Experience with Amazon Bedrock What we're looking for Strong MLOps engineering background Experience deploying ...

platform Building robust infrastructure and CI/CD pipelines End-to-end orchestration of ML workflows Working across AWS (SageMaker), Snowflake, GitLab, and MLflow Contributing to platform design, best practices, and production readiness (Bonus) Experience with Amazon Bedrock What we're looking for Strong MLOps engineering background Experience deploying ...

platform Building robust infrastructure and CI/CD pipelines End-to-end orchestration of ML workflows Working across AWS (SageMaker), Snowflake, GitLab, and MLflow Contributing to platform design, best practices, and production readiness (Bonus) Experience with Amazon Bedrock What we're looking for Strong MLOps engineering background Experience deploying ...

platform Building robust infrastructure and CI/CD pipelines End-to-end orchestration of ML workflows Working across AWS (SageMaker), Snowflake, GitLab, and MLflow Contributing to platform design, best practices, and production readiness (Bonus) Experience with Amazon Bedrock What we're looking for Strong MLOps engineering background Experience deploying ...

platform Building robust infrastructure and CI/CD pipelines End-to-end orchestration of ML workflows Working across AWS (SageMaker), Snowflake, GitLab, and MLflow Contributing to platform design, best practices, and production readiness (Bonus) Experience with Amazon Bedrock What we're looking for Strong MLOps engineering background Experience deploying ...

platform Building robust infrastructure and CI/CD pipelines End-to-end orchestration of ML workflows Working across AWS (SageMaker), Snowflake, GitLab, and MLflow Contributing to platform design, best practices, and production readiness (Bonus) Experience with Amazon Bedrock What we're looking for Strong MLOps engineering background Experience deploying ...

platform Building robust infrastructure and CI/CD pipelines End-to-end orchestration of ML workflows Working across AWS (SageMaker), Snowflake, GitLab, and MLflow Contributing to platform design, best practices, and production readiness (Bonus) Experience with Amazon Bedrock What we're looking for Strong MLOps engineering background Experience deploying ...

platform Building robust infrastructure and CI/CD pipelines End-to-end orchestration of ML workflows Working across AWS (SageMaker), Snowflake, GitLab, and MLflow Contributing to platform design, best practices, and production readiness (Bonus) Experience with Amazon Bedrock What we're looking for Strong MLOps engineering background Experience deploying ...