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The ideal candidate will have a strong foundation in classical Machine Learning/Data Science, with substantial hands-on experience building ML solutions before the recent GenAI wave, and should have subsequently expanded their expertise into Generative AI, LLM, and RAG-based applications.\n\nKey Responsibilities :\n\n- Design, develop, and productionize machine learning solutions for real-world business problems.\n\n- Build supervised and unsupervised ML models across areas such as classification, regression, forecasting/time-series, clustering, and anomaly detection.\n\n- Perform end-to-end data preparation, feature engineering, model development, validation, tuning, and evaluation.\n\n- Develop reusable and production-quality Python and SQL code.\n\n- Build and maintain automated training and inference pipelines.\n\n- Deploy and support ML models in production environments, ensuring reproducibility, versioning, and monitoring.\n\n- Provide hands-on technical leadership and guidance to other ML engineers/data scientists.\n\n- Develop and support GenAI/LLM/RAG applications, building on a strong classical ML foundation.\n\nRequired Skills & Experience :\n\n- 5+ years of hands-on experience in Machine Learning/Data Science/ML Engineering.\n\n- Strong practical experience with classical machine learning and production deployment.\n\n- Strong Python and SQL programming skills.\n\n- Experience with ML training and inference pipelines.\n\n- Experience with Git/version control and code reviews.\n\n- Recent hands-on experience with GenAI/LLM/RAG applications.\n\n- Strong problem-solving and technical leadership capabilities.\n\nGood to Have :\n\n- Experience with cloud platforms (AWS, Azure, GCP) and on-premise environments.\n\n- Experience with MLOps platforms, Docker/Kubernetes, and model serving.\n\n- Experience with LLM evaluation frameworks and RAG architectures.\n\nSkills\nMachine Learning, Data Science, Python, SQL, Generative AI, LLM, RAG, Model Validation, 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