1 to 25 of 40 MLflow Jobs in the UK excluding London

inference and training across GPUs and cloud-based architectures. Ensure security and compliance for ML platforms handling sensitive data. Evaluate and integrate MLOps tools (MLflow, Kubeflow, etc.) to enhance efficiency. Implement monitoring and alerting systems to detect anomalies and maintain model reliability. What We’re Looking For 3+ years of More ❯

managed AI/ML services Practical experience with Docker, Kubernetes, and container orchestration Databricks expertise, including ML workflows and data pipeline integration Familiarity with MLflow, DVC, Prometheus, and Grafana for versioning and monitoring Bachelor's or Master's degree in Computer Science, Engineering, or related field (or equivalent experience) Fluency More ❯

C# or Java, cloud SDKs and APIs. AI/ML expertise for pipeline efficiency, familiar with TensorFlow, PyTorch, AutoML, Python/R, and MLOps (MLflow, Kubeflow). Solid in DevOps, CI/CD automation with Bitbucket Pipelines, Azure DevOps, GitHub. Automate deployment of data pipelines and applications using Bash, PowerShell More ❯

cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Hands-on experience with CI/CD pipelines, version control, and ML workflows (MLflow, Kubeflow). Proven track record of delivering ML models that solve real-world business challenges at scale. Excellent communication skills with the ability to work More ❯

and data modeling. Familiarity with cloud platforms (AWS, Azure, GCP) for deploying ML and data solutions. Knowledge of MLOps practices and tools, such as MLflow or Kubeflow. Strong problem-solving skills, with the ability to troubleshoot both ML models and data systems. A collaborative mindset and ability to work in More ❯

security best practices . Prior experience with cloud-based lakehouse implementation . Experience with machine learning (ML) and AI frameworks, particularly within Databricks (e.g., MLflow, AutoML, or PyTorch/TensorFlow). Understanding of AI-driven analytics and predictive modeling to enhance BI solutions. Think you can make a difference? WE More ❯

Environment: Python, PyTorch or TensorFlow AWS (including SageMaker, S3, Lambda) or Azure ML Docker, Kubernetes, Airflow CI/CD tools (e.g. GitHub Actions, Jenkins) MLflow or similar frameworks Required Experience: Proven track record of deploying machine learning models to production Strong programming skills in Python and familiarity with modern ML More ❯

Desirable Experience of Microsoft Fabric Experience of Microsoft Dynamics Experience with data governance and compliance frameworks Proficiency in AI/ML frameworks (TensorFlow, PyTorch, MLflow) Knowledge of Agile and DevOps methodologies Other Stuff Benefits include 25 Days Annual Leave, Medical Insurance, Flexible Hybrid/Home Working, 5% Employer Contribution Pension More ❯

Desirable Experience of Microsoft Fabric Experience of Microsoft Dynamics Experience with data governance and compliance frameworks Proficiency in AI/ML frameworks (TensorFlow, PyTorch, MLflow) Knowledge of Agile and DevOps methodologies Other Stuff Benefits include 25 Days Annual Leave, Medical Insurance, Flexible Hybrid/Home Working, 5% Employer Contribution Pension More ❯

Desirable Experience of Microsoft Fabric Experience of Microsoft Dynamics Experience with data governance and compliance frameworks Proficiency in AI/ML frameworks (TensorFlow, PyTorch, MLflow) Knowledge of Agile and DevOps methodologies Other Stuff Benefits include 25 Days Annual Leave, Medical Insurance, Flexible Hybrid/Home Working, 5% Employer Contribution Pension More ❯

PostgreSQL Databricks Containerisation: Docker, Kubernetes CI/CD: Azure DevOps, GitHub Actions Relational databases and data lake architecture Model and data pipeline integration (e.g. MLflow) Microsoft Azure (Functions, Storage, Compute) Monitoring tools (Grafana, Prometheus, etc.) Mentoring and knowledge sharing within the team Senior Engineer - Desirable Skills: Experience in energy supply More ❯

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 ❯

proficiency in Python, including PyTorch, Hugging Face Transformers, Pandas, and scikit-learn. Strong understanding of cloud services (preferably AWS) and experience with Docker, Kubernetes, MLFlow, Kubeflow, or similar MLOps tools. 📩 Interested? Apply below or email me at mmatysik@trg-uk.com. More ❯

years leading Databricks-based solutions. Proven experience in a consulting environment delivering large-scale data platform projects. Hands-on expertise in Spark, Delta Lake, MLflow, Unity Catalog, and DBSQL. Strong proficiency in Python, SQL, and at least one major cloud platform (AWS, Azure, or GCP). Excellent communication skills and More ❯

technologies like AWS and Databricks to deliver scalable, robust solutions.. AI/ML Lifecycle Awareness : Familiarity with the machine learning lifecycle (e.g., tools like MLflow, Data Bricks Model Serving) and its integration into production systems. Collaboration : Strong interpersonal skills with the ability to work effectively across teams, including specialists in More ❯

with CI/CD pipelines and infrastructure-as-code tools (Terraform is a plus). Experience with Airflow or similar orchestration tools. Familiarity with MLflow or MLOps practices. Knowledge of data warehousing solutions (Snowflake, Redshift, BigQuery). Consulting background is a plus. Strong communication skills (oral & written) Rights to work More ❯

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 ❯

/GCP. · Ability to manage cloud infrastructure to ensure high availability, scalability, and cost efficiency. Nice-to-Have · Experience with ML orchestration platforms like MLflow, SageMaker Pipelines, Kubeflow, or similar. · Familiarity with model quantization, pruning, or other performance optimization techniques. · Exposure to distributed training frameworks like Unsloth, DeepSpeed, Accelerate, or More ❯

Natural Language Processing and Computer Vision Strong grasp of basic probability concepts and machine learning lifecycle Experience with workflow and pipelining frameworks (e.g., Kubeflow, MLFlow, Argo) Understanding and application of Ethical AI considerations Ready to take your career to the next level? Apply today and be part of something extraordinary More ❯

analysis. Expertise in Python and the tools and libraries that make ML magic happen. Familiarity with ML experiment tracking and collaboration tools, such as Mlflow and Weights & Biases. A solid background in software engineering and DevOps practices, MLOps deployment, and infrastructure. A knack for generative AI frameworks and SDKs, like More ❯

NLP unstructured data. Implemented ML platform in enterprise client experience of ML use-cases AIML Platforms: DataRobot, Dataiku, Anaconda, Sagemaker, Vertex etc. AIML Frameworks: MLFlow, KubeFlow, BentoML, TensorFlow etc. AIML Development: PyTorch, Jupyter Notebooks, XGBoost, Tensorflow etc. AIML Models: Linear/Logistic Regression, KNN, Decision Trees, Anomaly Detection, LLMs, Generative More ❯

. This role is ideal for someone who enjoys blending technical precision with innovation. You’ll: Build and manage ML pipelines in Databricks using MLflow, Delta Lake, Spark, and Mosaic AI. Train and deploy generative models (LLMs, GANs, VAEs) for NLP, content generation, and synthetic data. Architect scalable solutions using More ❯

audiences Nice-to-Have: Experience with marketing data, customer-level modelling, or decision science (e.g. uplift, attribution, causal AI, optimization) Familiarity with MLOps tooling (MLflow, FastAPI, Airflow, etc.) Experience designing and interpreting A/B tests or other experimental frameworks Background in consulting, agency, or fast-paced environments where autonomy More ❯

PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering More ❯

PRA SS1/21, FCA PS21/3, DORA, NIST AI RMF). Experience working with AI/ML platforms and monitoring tools (e.g., MLflow , Azure ML, SageMaker , Databricks , Python etc ). Excellent stakeholder management and communication skills. Desirable: Technical background in AI/ML, data science, or software engineering More ❯