Responsibilities:
- 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
Qualifications:
- 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
Summary
Deploy ML models and AI pipelines from PoC to scalable production with CI/CD, monitoring, and optimization for inference speed and efficiency. Requires Bachelor's or Master's in Computer Science or related field with 5-9 years experience, proficiency in Python, PyTorch, and MLOps tooling.
Responsibilities
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
Qualifications
Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, AI or related field; Proficiency in Python and libraries such as PyTorch, NumPy, Pandas and Scikit-learn; Knowledge of model deployment/containerisation and SQL/NoSQL databases
Education requirements
Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, AI or related field
Experience requirements
5-9 years of relevant experience