12 of 12 MLflow Jobs in the South East

and SQL, inc. the following libraries: Numpy, Pandas, PySpark and Spark SQL Expert knowledge of ML Ops frameworks in the following categories: experiment tracking and model metadata management (e.g. MLflow) orchestration of ML workflows (e.g. Metaflow) data and pipeline versioning (e.g. Data Version Control) model deployment, serving and monitoring (e.g. Kubeflow) Expert knowledge of automated artefact deployment using YAML based More ❯

prompt engineering (e.g., GPT, BERT, T5 family). • Familiarity with on-device or edge-AI deployments (e.g., TensorFlow Lite, ONNX, mobile/embedded inference). • Knowledge of MLOps tooling (MLflow, Weights & Biases, Kubeflow, or similar) for experiment tracking and model governance. • Open-source contributions or published papers in top-tier AI/ML conferences (NeurIPS, ICML, CVPR, ACL, etc.). More ❯

Strong understanding of SQL, NoSQL, 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 a fast-paced, small team More ❯

as TensorFlow, PyTorch, Scikit-learn, and Keras. Understanding of algorithms and techniques for supervised and unsupervised learning. Experience with tools for model monitoring, logging, and performance evaluation, such as MLflow or Prometheus. Strong scripting skills in Bash, PowerShell, or similar scripting languages for automation of tasks and ability to write reusable and maintainable code to streamline ML operations Proficient in More ❯

as TensorFlow, PyTorch, Scikit-learn, and Keras. Understanding of algorithms and techniques for supervised and unsupervised learning. Experience with tools for model monitoring, logging, and performance evaluation, such as MLflow or Prometheus. Strong scripting skills in Bash, PowerShell, or similar scripting languages for automation of tasks and ability to write reusable and maintainable code to streamline ML operations Proficient in 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 #J-18808-Ljbffr More ❯

and API designs for ML model deployments. Strong Python skills, particularly in relevant data libraries. Cloud engineering experience, particularly with AWS and Databricks. Additional experience in GenAI/NLP, MLflow, Jenkins, workflow automation, AutoML, unit testing, and model explainability is a plus! This is your chance to elevate your career and work on impactful ML projects! If you're excited 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 ❯

ability to translate complex analyses into actionable insights. Nice-to-Haves: Familiarity with marketing-specific measurement models such as Media Mix Modelling (MMM). Knowledge of model versioning (e.g. MLFlow), API frameworks (FastAPI), or building dashboards (e.g. Dash or Streamlit). The Opportunity: You’ll work across multiple industries and household-name brands, contributing to meaningful campaigns powered by cutting More ❯

occasions. To be a successful, the ideal Machine Learning Engineer candidate will have: · Highly skilled in Python. · Knowledge of AWS or GCP. · Ideally experience of SKLearn/Docker/MLFlow or PyTest · Excellent communication and problem solving skills. What is in it for you? As a talented Machine Learning Engineer you can expect: · Great salary - Up to £80,000 base More ❯

occasions. To be a successful, the ideal Machine Learning Engineer candidate will have: · Highly skilled in Python. · Knowledge of AWS or GCP. · Ideally experience of SKLearn/Docker/MLFlow or PyTest · Excellent communication and problem solving skills. What is in it for you? As a talented Machine Learning Engineer you can expect: · Great salary - Up to £80,000 base More ❯

interactive AI demos and proofs-of-concept with Streamlit, Gradio, or Next.js for stakeholder feedback; MLOps & Deployment: Implement CI/CD pipelines (e.g., GitLab Actions, Apache Airflow), experiment tracking (MLflow), and model monitoring for reliable production workflows; Cross-Functional Collaboration: Participate in code reviews, architectural discussions, and sprint planning to deliver features end-to-end. Requirements: Master’s degree in More ❯