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extraction, and domain-specific reasoning\nImplement and maintain MLOps and LLMOps pipelines, including model training, versioning, CI/CD, deployment, rollback, and lifecycle management\nDevelop model monitoring and observability solutions covering performance, drift detection, bias, latency, and cost metrics\nApply AIOps concepts to automate detection, root cause analysis, and predictive insights using operational and telemetry data\nCollaborate with API Manager and platform teams to expose ML/AI capabilities as secure, scalable, and well-documented APIs\nParticipate in data preparation and feature engineering, working closely with data engineering teams and feature stores\nPerform rigorous model validation, experimentation, and benchmarking, ensuring reliability and reproducibility\nContribute to technical design documents, architecture reviews, and best-practice guidelines\nMentor junior engineers/interns and contribute to raising overall data science and engineering standards within the team\nStay up to date with advancements in machine learning, deep learning, and generative AI, and assess their applicability to business use cases\nPerson Specifications\nBachelor's degree in IT/Computer Science, Data Science, Engineering, Mathematics, or a related field\n03+ years of hands-on experience in data science or machine learning engineering roles\nStrong experience with Python and common ML/DL libraries (scikit-learn, PyTorch, TensorFlow, NumPy, pandas)\nProven experience developing and deploying production-grade ML models\nHands-on experience with MLOps platforms and tools (e.g., MLflow, Kubeflow, SageMaker, Vertex AI, or equivalent)\nPractical exposure to LLMOps, including prompt engineering, fine-tuning, evaluation, and model serving\nExperience working with APIs, microservices, and integrating ML models into enterprise applications\nSolid understanding of data pipelines, feature engineering, and model lifecycle management\nExperience with cloud platforms 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