AI Engineer - Generative AI, LLM & MLOps (Karnataka)

Key Responsibilities - Design, develop, and deploy machine learning, statistical, deep learning, and Generative AI models for enterprise use cases. - Perform data exploration, feature engineering, model training, evaluation, and optimization on structured and unstructured datasets. - Build predictive, prescriptive, and descriptive analytics solutions aligned with business objectives. - Develop and fine-tune AI/ML models including classical ML, Deep Learning, NLP, Time-Series Forecasting, and Large Language Models (LLMs). - Design and implement Retrieval-Augmented Generation (RAG) pipelines using enterprise knowledge sources and Vector Databases. - Build reusable Prompt Templates and Prompt Engineering strategies for enterprise AI applications. - Apply Anthropic Constitutional AI principles to develop protected, reliable, and responsible AI applications. - Collaborate with Data Engineering and Platform teams to productionize models using MLOps and LLMOps best practices. - Develop AI experimentation, evaluation, and benchmarking frameworks using MLflow and enterprise AI evaluation methodologies. Key Responsibilities - Design, develop, and deploy machine learning, statistical, deep learning, and Generative AI models for enterprise use cases. - Perform data exploration, feature engineering, model training, evaluation, and optimization on structured and unstructured datasets. - Build predictive, prescriptive, and descriptive analytics solutions aligned with business objectives. - Develop and fine-tune AI/ML models including classical ML, Deep Learning, NLP, Time-Series Forecasting, and Large Language Models (LLMs). - Design and implement Retrieval-Augmented Generation (RAG) pipelines using enterprise knowledge sources and Vector Databases. - Build reusable Prompt Templates and Prompt Engineering strategies for enterprise AI applications. - Apply Anthropic Constitutional AI principles to develop protected, reliable, and responsible AI applications. - Collaborate with Data Engineering and Platform teams to productionize models using MLOps and LLMOps best practices. - Develop AI experimentation, evaluation, and benchmarking frameworks using MLflow and enterprise AI evaluation methodologies.