Principal Data Scientist - R01559946, Bengaluru

Key Responsibilities: Agentic AI System Design&Development Design and build multi-agent systems, autonomous agents, and tool-using AI workflows. Develop orchestration frameworks for agent collaboration using LLM-based architectures. Implement human-in-the-loop and decision-support systems for enterprise applications. Optimize agent performance for reliability, scalability, and cost efficiency.LLM Engineering&Productionization Fine-tune and prompt-engineer Large Language Models (open-source and proprietary). Build and deploy production-grade LLM applications including copilots, chat systems, and intelligent automation tools. Design and implement Retrieval-Augmented Generation (RAG) pipelines. Evaluate and optimize LLM performance across accuracy, latency, and cost metrics.GenAI Frameworks&Ecosystem Work with frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, and AutoGen. Integrate LLMs with enterprise data sources and APIs for real-world use cases. Develop reusable GenAI components and scalable architecture patterns. Stay current with emerging GenAI tools and research advancements.Data Science&Machine Learning Translate business problems into well-defined ML and AI solutions. Perform advanced data wrangling, feature engineering, and exploratory data analysis. Apply statistical modeling, deep learning, NLP, and machine learning techniques. Drive experimentation, hypothesis testing, and model evaluation.MLOps / LLMOps&Production Systems Design and implement CI/CD pipelines for ML and LLM systems. Ensure model monitoring, versioning, governance, and performance tracking. Optimize infrastructure for scalable deployment on cloud platforms (AWS, Azure, or GCP). Implement responsible AI practices including explainability, fairness, and risk management.Insight Generation&Stakeholder Communication Translate complex analytical and AI outputs into actionable business insights. Build data storytelling narratives for executive leadership. Influence strategic decisions through AI-driven recommendations. Communicate technical concepts clearly to non-technical stakeholders.Leadership&Mentorship Lead and mentor senior data scientists and ML engineers. Define best practices for AI development, deployment, and governance. Drive capability building in GenAI, Agentic AI, and advanced analytics. Foster a culture of innovation, ownership, and continuous learning. Innovation&Problem Solving Apply design thinking to develop user-centric AI solutions. Evaluate trade-offs between model complexity, performance, and business value. Lead experimentation with cutting-edge AI techniques and architectures. Identify opportunities for AI-driven transformation across business domains.