Deploying, monitoring, and scaling machine learning models in production.
Collaborating with data scientists, engineers, and stakeholders to integrate AI solutions into scalable products.
Supporting the full ML lifecycle, from experimentation to deployment and optimisation.
Applying best practices in data engineering and contributing to architectural decisions.
Using modern MLOps tools and CI/CD approaches to improve reliability and efficiency.
Contributing to a culture of knowledge-sharing and continuous improvement.

Strong Python skills and familiarity with ML libraries such as Pandas, NumPy, and scikit-learn.
Experience with frameworks such as TensorFlow, Keras, or PyTorch.
Exposure to gradient boosting tools such as XGBoost, LightGBM, or CatBoost.
Experience with model deployment tools (e.g., ONNX, TensorRT, TensorFlow Serving, TorchServe).
Familiarity with ML lifecycle tools such as MLflow, Kubeflow, or Azure ML Pipelines.
Experience working with distributed data processing (e.g., PySpark) and SQL.
Understanding of software engineering best practices, including version control (Git).
Knowledge of CI/CD principles in ML environments.
Experience with cloud-native ML platforms is advantageous.

Competitive salary
Pension scheme
DXC Select – comprehensive benefits package including private medical insurance, gym membership, and more.
Perks at Work – discounts on technology, groceries, travel and more.
DXC incentives – recognition tools, employee lunches, and regular social events.

Data/Machine Learning Ops Engineer

Key skills