1 to 25 of 43 Permanent MLflow Jobs in London

language models. Comfortable with cloud platforms (Azure preferred), CI/CD tools, and containerization (Docker, Kubernetes). Experience with monitoring and maintaining ML systems in production, using tools like MLflow, Weights & Biases, or similar. Strong communication skills and ability to work across disciplines with ML scientists, engineers, and stakeholders. Preferred Qualifications PhD in Computer Science, Machine Learning, Engineering , or a More ❯

/or real-time systems Have knowledge of DevOps technologies such as Docker and Terraform, building APIs, CI/CD processes and tools, and MLOps practices and platforms like MLFlow and monitoring Have experience with agile delivery methodologies Have good communication skills Have an advanced degree in Computer Science, Mathematics or a similar quantitative discipline Nice to have Hands-on … technology stack Python and associated ML/DS libraries (Scikit-learn, Numpy, LightlGBM, Pandas, TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, S3, Athena, etc. MLOps: Terraform, Docker, Airflow, MLFlow, Jenkins On call statement: Please be aware that our Machine Learning Engineers are required to be a part of the technology on-call rota. More details on how this works More ❯

Python and associated ML/DS libraries (Scikit-learn, Numpy, LightlGBM, Pandas, LangChain/LangGraph, TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc. MLOps: Terraform, Docker, Airflow, MLFlow More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, an EV Scheme to further reduce carbon emissions More ❯

Python and associated ML/DS libraries (scikit-learn, numpy, LightlGBM, Pandas, LangChain/LangGraph TensorFlow, etc ) PySpark AWS cloud infrastructure: EMR, ECS, Athena, etc. MLOps: Terraform, Docker, Airflow, MLFlow More information: Enjoy fantastic perks like private healthcare & dental insurance, a generous work from abroad policy, 2-for-1 share purchase plans, an EV Scheme to further reduce carbon emissions More ❯

The Machine Learning (ML) Practice team is a highly specialized customer-facing ML team at Databricks facing an increasing demand for Large Language Model (LLM)-based solutions. We deliver professional services engagements to help our customers build, scale, and optimize More ❯

and ETL processes Good knowledge of ML ops principles and best practices to deploy, monitor and maintain machine learning models in production Familiarity with Git CI/CD and MLflow for managing and tracking code deployment or model versions Experience with cloud-based data platforms such as AWS or Google Cloud Platform Nice to have: Experience with Kafka Proven track More ❯

a related discipline, with a BSc required and an MSc considered advantageous. Experienced in using machine learning frameworks such as Scikit-learn, Keras, and PyTorch, with additional familiarity with MLFlow and AzureML seen as a positive. Have working knowledge of CI/CD practices, ML Ops, ML pipelines, automated testing, and platforms such as AzureML, Google Cloud, or AWS. Possess More ❯

Lead Machine Learning Engineer - LLMs - Ramboll Tech At Ramboll Tech, we believe innovation thrives in diverse, supportive environments where everyone can contribute their best ideas. As a Lead Machine Learning Engineer, you will create cutting-edge AI solutions, mentor others More ❯

What You'll Do - Design and build an end-to-end MLOps pipeline using AWS , with a strong focus on SageMaker for training, deployment, and hosting. - Integrate and operationalize MLflow for model versioning, experiment tracking, and reproducibility. - Architect and implement a feature store strategy for consistent, discoverable, and reusable features across training and inference environments (e.g., using SageMaker Feature Store … years of experience in MLOps, DevOps, or ML infrastructure roles. - Deep familiarity with AWS services , especially SageMaker , S3, Lambda, CloudWatch, IAM, and optionally Glue or Athena. - Strong experience with MLflow , experiment tracking , and model versioning. - Proven experience setting up and managing a feature store , and driving best practices for feature engineering in production systems . - Proficiency in model testing strategies More ❯

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 🎓 Bonus points for: MSc/PhD in ML or AI Databricks ML Engineer (Professional) certified More ❯

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 🎓 Bonus points for: MSc/PhD in ML or AI Databricks ML Engineer (Professional) certified More ❯

mentoring and managing data science teams. Deep knowledge of media measurement techniques, such as media mix modelling. Experience with advanced AI techniques, including NLP, GenAI, and CausalAI. Familiarity with MLFlow, API design (FastAPI), and dashboard building (Dash). If this role looks of interest, reach out to Joseph Gregory More ❯

mentoring and managing data science teams. Deep knowledge of media measurement techniques, such as media mix modelling. Experience with advanced AI techniques, including NLP, GenAI, and CausalAI. Familiarity with MLFlow, API design (FastAPI), and dashboard building (Dash). If this role looks of interest, reach out to Joseph Gregory More ❯

mentoring and managing data science teams. Deep knowledge of media measurement techniques, such as media mix modelling. Experience with advanced AI techniques, including NLP, GenAI, and CausalAI. Familiarity with MLFlow, API design (FastAPI), and dashboard building (Dash). If this role looks of interest, reach out to Joseph Gregory More ❯

Coding Skills: Proficient in Python, SQL, and one of Pytorch, Tensorflow, Scikit-learn, with daily experience in writing, debugging, and optimising code. ML Ops Knowledge: Familiarity with tools like MLflow, Kubeflow, or Vertex AI, and experience implementing CI/CD pipelines for machine learning. Understanding of Financial Services: Financial Services understanding is a plus, ideally in a lending environment. Strong More ❯

on-premise and cloud environments to handle text and audio data processing loads for ML models Deploy NLP models in cloud environments (AWS SageMaker) through Jenkins Design and implement MLflow and other ML Ops applications to streamline ML workflows which adhere to strict data privacy and residency guidelines Communicate your work throughout the team and related departments Mentor and guide More ❯

complex property management workflows and decision-making processes Hands-on experience with AI/ML frameworks (TensorFlow, PyTorch), LLM orchestration tools (LangChain, LangGraph), MLOps practices and tooling (such as MLflow, Kubeflow, or similar), vector databases, and cloud platforms (AWS, Azure, GCP) with their AI/ML offerings Preferably hands-on experience with voice technologies and computer vision for relevant property More ❯

development lifecycle with a strong focus on performance and maintainability. Collaborate cross-functionally with consulting and engineering teams to guide best practices. Drive innovation using tools such as Terraform, MLflow, AzureML, LangSmith, and more. Technical Requirements: Advanced proficiency in Python and modern software engineering practices. Experience architecting solutions using major cloud platforms (Azure, AWS, GCP). Familiarity with technologies such More ❯

enables you to take part in day-to-day conversations in the team and contribute to deep technical discussions Nice to Have Experience with operating machine learning models (e.g., MLFlow) Experience with Data Lakes, Lakehouses, and Warehouses (e.g., DeltaLake, Redshift) DevOps skills, including terraform and general CI/CD experience Previously worked in agile environments Experience with expert systems Perks More ❯

in programming languages such as Python or R, with extensive experience with LLMs, ML algorithms, and models. Experience with cloud services like Azure ML Studio, Azure Functions, Azure Pipelines, MLflow, Azure Databricks, etc., is a plus. Experience working in Azure/Microsoft environments is considered a real plus. Proven understanding of data science methods for analyzing and making sense of More ❯

but not essential NLP/Deep learning experience (e.g. huggingface, spaCy) Deep learning framework experience (preferably PyTorch) MLOps experience (e.g. data and model versioning, model deployment CI/CD, MLFlow/DVC) Cloud platform experience, especially from an ML standpoint (AWS preferred) Statistical testing experience Experience with AWS Bedrock Experience with C# Containerization via Docker. Awareness of basic data science More ❯

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 and analytics accessible to everyone. We More ❯

At Databricks, we are on a mission to empower our customers to solve the world's toughest data problems by utilising the Databricks Data Intelligence Platform. As a Delivery Solutions Architect (DSA), you will play an important role during this More ❯

At Databricks, our core values center on creating a proactive and customer-centric culture. We aim to develop a unified platform that makes data science and analytics accessible to all, inspiring our customers to make informed decisions that advance their More ❯

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 and analytics accessible to everyone. We More ❯