About the role / About the job
In this position, you will be responsible for deploying machine learning models and AI pipelines from proof of concept (PoC) through to scalable and reliable production, utilizing CI/CD and orchestration strategies. You will implement monitoring and maintenance strategies for deployed models, ensuring their optimal performance and reliability.
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You will also optimize models for inference speed and resource efficiency using techniques such as TensorRT, OpenVINO, ONNX, pruning, and quantisation. Additionally, you will perform data collection, cleaning, and feature engineering to prepare datasets for training. Collaboration is key in this role, as you will work closely with data scientists, engineers, and product managers, maintaining clear and thorough documentation.
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
- Optimize 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/containerization (Docker, Kubernetes) and SQL/NoSQL databases
- Familiarity with a cloud platform and MLOps tooling (MLflow, ClearML, etc.); edge deployment experience a plus
