Provide expert guidance on problem framing, data preparation, feature engineering, algorithm and model selection, experimental design, validation, explainability and performance trade-offs.
Design and review architectures for machine learning, forecasting, optimisation, generative AI, retrieval-augmented generation and agentic AI, selecting approaches based on accuracy, latency, scalability, maintainability, risk and cost.
Lead solutions from proof of concept to production, covering data pipelines, APIs, MLOps/LLMOps, versioning, CI/CD, observability, retraining, rollback, security and enterprise integration.
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