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  Design, train, and evaluate machine learning models (predictive, deep learning, or NLP/GenAI depending on project needs) to solve complex business problems.
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  Lead the development of scalable, robust, and automated end-to-end ML pipelines (data ingestion, feature engineering, training, and deployment).
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  Contribute to the architecture of our ML infrastructure, ensuring models are containerized, deployed efficiently, and easily integrated into core software products.
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  Implement continuous monitoring strategies to track model performance, data drift, and latency, driving iterative improvements post-deployment.
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  Conduct experimentation to evaluate new algorithms, open-source frameworks, and methodology advancements.
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  Partner with Data Scientists, Software Engineers, and Product Managers to translate business requirements into technical solutions.

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  A relevant tertiary qualification would be beneficial (Computer Science, Data Science, Machine Learning, etc.).
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  3 - 5+ years of professional experience developing, scaling, and deploying ML models in a production environment.
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  Strong proficiency in Python and ecosystem libraries (e.g., PyTorch, TensorFlow, scikit-learn, Pandas).
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  Hands-on experience with cloud platforms (AWS, GCP, or Azure) and MLOps tools (e.g., Docker, Kubernetes, MLflow, or cloud-native equivalents like SageMaker/Vertex AI).
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  Solid understanding of statistical modelling, feature engineering, and data manipulation skills (including strong SQL).
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  Familiarity with software engineering best practices, including Git version control, CI/CD pipelines, and writing clean, testable code.
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  Excellent communication skills with the ability to explain complex technical concepts to non-technical stakeholders.

Requirements