Skills

Full description

Deploy ML models and AI pipelines from PoC through to scalable, reliable production, via CI/CD and orchestration
Implement monitoring and maintenance strategies for deployed models
Optimise models for inference speed and resource efficiency (TensorRT, OpenVINO, ONNX, pruning, quantisation)
Perform data collection, cleaning and feature engineering to prepare datasets for training
Collaborate with data scientists, engineers and product managers; maintain clear documentation

Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, AI or related field
5-9 years of relevant experience
Proficiency in Python and libraries such as PyTorch, NumPy, Pandas and Scikit-learn
Knowledge of model deployment/containerisation (Docker, Kubernetes) and SQL/NoSQL databases
Familiarity with a cloud platform and MLOps tooling (MLflow, ClearML, etc.); edge deployment experience a plus

Deploy ML models and AI pipelines from PoC through to scalable, reliable production, via CI/CD and orchestration
Implement monitoring and maintenance strategies for deployed models
Optimise models for inference speed and resource efficiency (TensorRT, OpenVINO, ONNX, pruning, quantisation)
Perform data collection, cleaning and feature engineering to prepare datasets for training
Collaborate with data scientists, engineers and product managers; maintain clear documentation

Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, AI or related field
5-9 years of relevant experience
Proficiency in Python and libraries such as PyTorch, NumPy, Pandas and Scikit-learn
Knowledge of model deployment/containerisation (Docker, Kubernetes) and SQL/NoSQL databases
Familiarity with a cloud platform and MLOps tooling (MLflow, ClearML, etc.); edge deployment experience a plus