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High competition due to Tier-1 brand, Bangalore location, and popular ML/LLM role attracting many candidates.
Low because core ML/LLM, MLOps and cloud skills are highly transferable across industries.
High due to extensive mandatory ML/LLM, MLOps, cloud and specific tooling requirements.
Lead design, development, and deployment of machine learning and deep learning models for business applications, including generative AI and large language model (LLM) workflows.
Build and maintain robust data pipelines and ensure continuous model training, evaluation, and reliable production deployment using MLOps best practices.
Collaborate cross-functionally to integrate AI/ML solutions, implement evaluation and validation frameworks for generative AI for accuracy, safety, and cost-efficiency.
Hands-on experience with ML frameworks (TensorFlow, PyTorch, Keras, Scikit-learn) and integrating LLM provider APIs (Anthropic Claude, OpenAI, Azure OpenAI).
Experience building LLM workflows using orchestration tools such as LangGraph and LangChain.
Strong programming skills in Python, R, or Java; proficiency with SQL and data processing libraries (Pandas, NumPy); familiarity with cloud ML services (AWS, Azure, Google Cloud).
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in end-to-end ML lifecycle from data handling and model training to production deployment and MLOps in a cloud environment.
Technical expertise in cutting-edge generative AI concepts such as retrieval-augmented generation (RAG), prompt engineering, and LLM output validation.
Comfortable working in a complex, technology-driven environment requiring close cross-functional collaboration with data scientists, engineers, and product teams.