As a Machine Learning Engineer with a Master's in Data Science from the University of Michigan–Dearborn and 4+ years of hands-on experience across automotive, pharmaceutical, legal, and ed-tech domains, I thrive at the intersection of AI innovation, scalable architecture, and impactful business outcomes. Currently at Ford Motor Company, I’m leading the design and deployment of advanced machine learning systems—including LLM Retrieval-Augmented Generation (RAG), regression forecasting, and RESTful chat interfaces—leveraging tools like Vertex AI, BigQuery, FastAPI, etc. Previously, at Omdena, I directed a global ML team to build time-series forecasting models on 22M+ data points and deployed LLM-powered conversational agents. My experiences at Takeda Pharmaceuticals and L&T Infotech further solidified my expertise in cloud-based ML deployments (AWS, Azure), data pipelines (Airflow, PySpark), and NLP applications at scale. 🔹 Core Expertise: Python, SQL, Tensorflow, PyTorch, Scikit-learn, LLMs, RAG, Gen AI, XGBoost, A/B Testing 🔹 Tools: GCP, AWS SageMaker, Azure ML, FastAPI, LangGraph, Pinecone, Docker, Kubernetes, Terraform 🔹 Certifications: AWS certified Machine Learning Specialty, DP-100 Azure Data Scientist Associate, AI 102: Azure AI Engineer Associate I’m passionate about building real-world ML solutions that scale—be it for legal automation, mental health AI assistants, or intelligent document understanding. Always open to meaningful collaboration and new opportunities to drive AI-led transformation.