6 of 6 MLflow Jobs in the Midlands

and Gurobi. Other programming languages are a plus. Solid experience with SQL, data engineering, and cloud-based tools (AWS preferred), as well as version control (Git), experiment tracking (e.g. MLflow), and containerisation (e.g. Docker). Familiarity with CI/CD tools (e.g. GitHub Actions), model/data versioning (e.g. DVC), and orchestration frameworks (e.g. Airflow, Dagster). Skilled in testing More ❯

and data teams Optimise model performance for accuracy, speed, and cost Run experiments, A/B tests, and validate approaches statistically Use tools like Python, TensorFlow, PyTorch, Scikit-learn, MLflow, SQL, Docker, AWS/GCP What Youll Need: Bachelors degree in Computer Science, AI, Machine Learning, or related field 1+ year of experience in a Machine Learning Engineer or similar More ❯

Airflow, Dagster). Proficient in SQL and cloud platforms (AWS preferred), with exposure to model/data versioning tools (e.g. DVC), containerised solutions (Docker, ECS), and experiment tracking (e.g. MLflow). Demonstrated ability to frame business and technical problems, assess trade-offs, and select effective modelling approaches. Excellent communication skills and the ability to influence both technical and non-technical More ❯

workflows, containerisation (e.g., Docker, Kubernetes) and production-grade APIs Understanding of data governance, privacy and regulatory compliance (e.g., GDPR) Nice to have: Familiarity with Infrastructure as Code (e.g., Ansible), MLFlow, or orchestration frameworks Background in both object-oriented and functional programming paradigms Please note: Visa sponsorship is unfortunately not available for this role. Applicants must have the right to work More ❯

in data science roles, preferably in fast-moving or early-stage environments Languages & Tools: Strong Python (Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch) Advanced SQL AWS MLOps tooling (e.g., MLflow, SageMaker, or similar) Bonus Points For: Knowledge of LLMs, RAG pipelines, prompt engineering, or agentic interfaces Experience with data warehouse tools (Snowflake, Redshift, BigQuery) Familiarity with search, matching, or recommendation More ❯

in data science roles, preferably in fast-moving or early-stage environments Languages & Tools: Strong Python (Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch) Advanced SQL AWS MLOps tooling (e.g., MLflow, SageMaker, or similar) Bonus Points For: Knowledge of LLMs, RAG pipelines, prompt engineering, or agentic interfaces Experience with data warehouse tools (Snowflake, Redshift, BigQuery) Familiarity with search, matching, or recommendation More ❯