Data / Machine Learning Ops Engineer

The Data position centres on using modern MLOps tools and CI/CD approaches to improve reliability and efficiency. It would suit someone who can bring careful technical judgement and practical problem-solving to the role.

How the role is set up

Strong Python skills and familiarity with ML libraries such as Pandas, NumPy, and scikit-learn. Exposure to gradient boosting tools such as XGBoost, LightGBM, or CatBoost.

Where someone would start

Using modern MLOps tools and CI/CD approaches to improve reliability and efficiency. Deploying, monitoring, and scaling machine learning models in production. Collaborating with data scientists, engineers, and stakeholders to integrate AI solutions into scalable products.

What is expected

• Experience with frameworks such as TensorFlow, Keras, or PyTorch.
• Experience with model deployment tools (e.g., ONNX, TensorRT, TensorFlow Serving, TorchServe).
• Experience working with distributed data processing (e.g., PySpark) and SQL.

Job details

• This role is ideal for someone who enjoys solving complex problems, working cross‑functionally, and continuously developing their technical expertise in a supportive environment.
• Are you passionate about bringing machine learning solutions into real‑world production environments?
• If you don’t meet every single requirement listed below, we still encourage you to apply.
• We value potential, curiosity, and a willingness to learn.