Bhargavi Karuku

About Bhargavi Karuku

AI/ML Engineer with 4+ years of experience designing, training, and deploying production-grade machine learning, deep learning, and LLM-based systems across enterprise environments. Proven ability to translate complex business problems into scalable AI solutions with measurable impact on cost reduction, automation efficiency, and decision accuracy.Strong expertise in Python and modern ML frameworks, with hands-on experience building scalable, end-to-end ML pipelines from data ingestion to model inference.Hands-on experience building end-to-end ML pipelines, from data ingestion and feature engineering to containerized deployment on Kubernetes, model monitoring, and CI/CD-driven releases. Strong background in LLM applications (RAG, agents, semantic search), model optimization, and cloud-native MLOps across AWS, Azure, and GCP.Skilled in building high-performance training and inference pipelines, integrating ML models into Java/C++ backend systems, and developing low-latency RESTful APIs and microservices for real-time AI-driven decisioning.Hands-on experience with MLOps and cloud-native deployment, leveraging AWS SageMaker, Azure ML, and GCP Vertex AI for distributed training, experimentation, and scalable production deployments.Applying cutting-edge AI/ML research, including transformers, diffusion models, model distillation, and mixture-of-experts to develop innovative, production-ready systems that drive business impact.

Experience

1. Ai Ml Engineer

   Dell Technologies

   Feb 2025 — Present · US

   Led development of enterprise ML and generative AI systems including LLM-powered agents and RAG applications for supply chain and customer supportDesigned and deployed 12+ production ML models using PyTorch, TensorFlow, and scikit-learn, improving forecast accuracy by 18% and decision coverage by 25%Built LLM-powered RAG applications using LangChain, LangGraph, and FAISS that reduced manual resolution time by 30%Architected containerized ML services using Docker and Kubernetes with auto-scaling for low-latency inference at enterprise scaleImplemented CI/CD pipelines using GitHub Actions and Azure DevOps, reducing release cycles by 35%Optimized inference performance through batching and quantization, reducing GPU costs by 22%Integrated ML models into Java/C++ backend systems via FastAPI microservices for real-time scoringEstablished model governance practices using SHAP and built monitoring dashboards for data drift and performance tracking

Education

• Anil Neerukonda Institute Of Technology & Sciences

  Bachelor of Technology - BTech, Computer Science

• University of New Haven

  Master's degree, Data Science