Principal/Sr Principal Machine Learning Engineer

Led a team of 5 data scientists to design and deploy scalable Generative AI systems that improved product functionality across multiple industries. Managed end-to-end project lifecycles, ensuring timely delivery of high-quality AI solutions. Assisted in pre-sales by analyzing client needs, crafting tailored solutions, delivering presentations, and supporting deal closures through expertise and collaboration. Key Achievements: Designed classical machine learning models using Linear regression,Random Forest,XGBoost etc based predicton models, reducing error by 20%. Implemented causal ML in energy cost predictions. Developed a Natural Language to SQL generator that increased query efficiency by 40%, enhancing user interaction with databases. Fine-tuned custom Small Language Models (SLMs), achieving a 30% improvement in retrieval accuracy for RAG applications. Created a multi-agent content generation system using MetaGPT and Autogen that reduced content creation time by 50%, significantly improving productivity. Implemented predictive analytics models that led to a 20% reduction in operational costs for clients in the cement industry. Gen AI Consultant for various projects, driving AI innovations across industries. Designed and deployed scalable Gen AI systems using Azure OpenAI and Semantic Kernel. Documented AI chat systems with Azure OpenAI using the RAG pattern ,Knowledge Graphs and LLM. Developed Natural Language to SQL query generation for databases with 200+ objects. Fine-tuned custom Small Language Models (SLMs) for RAG using Tinyllama. Deployed and hosted custom LLMs on Azure, ensuring efficient operation. Led the design and development of machine learning models for predictive analytics. Implemented operational research optimization using PuLP and Gurobi for complex problem solving. Optimized energy costs in the cement industry through data science and machine learning. Led NLP-driven customer support optimization using Python, NLTK, and spaCy. Developed sentiment analysis and topic classification models for customer support. Deployed machine learning models using Docker containers for efficient operations. Created automated data ingestion pipelines with Azure Data Factory ,Synapse and Logic Apps. Integrated Azure Cognitive Services for language translation in data processing. Led the development of computer vision models for worker safety in construction using OpenCV and TensorFlow. Leveraged AI for hazard detection in the construction industry, improving safety practices. Developed a multi-agent content generation system using MetaGPT and Autogen. Enhanced targeted marketing by improving dealer classification accuracy using machine learning. Implemented prescriptive analytics to guide strategic decision-making in business operations using Pulp and Gurobi frameworks. Utilized big data frameworks like Azure Databricks and Spark for large-scale data processing. Deployed real-time data processing systems using Azure and Python-based frameworks. Managed data science pipelines from data collection to model deployment. Used Docker and Kubernetes for scalable, containerized machine learning deployments. Positioned organizations as industry leaders through AI-driven innovation. Fostered a data-driven culture within organizations, driving business growth through analytics.

Lilly is a global healthcare leader that unites caring with discovery to create medicines that make life better for people around the world. We were founded more than a century ago by a man committed to creating high-quality medicines that meet real needs. Global Immunology, Neuroscience, Oncology, Diabetic, Acute Care and Retirement Health R&D centers help achieve this purpose. As we build on our rich history, we are now focused on discoveries that will lead to breakthrough treatments for many of the world's most urgent medical needs in immunology, oncology, neuroscience and more.