Design, develop, and deploy machine learning models using modern frameworks and libraries
Collaborate closely with data scientists, engineers, and stakeholders to turn ideas into impactful solutions
Optimize and deploy models with tools like TensorFlow Serving, TorchServe, ONNX, and TensorRT
Build and manage ML pipelines using MLflow, Kubeflow, and Azure ML Pipelines
Work with large-scale data using PySpark and integrate ML solutions into production environments
Monitor and improve model performance to ensure accuracy and efficiency
Contribute to team knowledge by mentoring and supporting colleagues
Bring creativity and fresh perspectives to problem-solving and technical solutions

Strong Python skills and experience with ML libraries (pandas, NumPy, scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, Keras, PyTorch)
Familiarity with model deployment and serving tools (ONNX, TensorRT, TensorFlow Serving, TorchServe)
Experience with ML lifecycle tools (MLflow, Kubeflow, Azure ML Pipelines)
Knowledge of distributed data processing (PySpark) and software engineering principles (Git)
A collaborative mindset and excellent problem-solving abilities
Experience in data cleansing, exploratory data analysis, and visualisation
A continuous learning mindset and interest in emerging AI/ML technologies

Machine Learning Engineer

Key skills