Bridging the gap between cutting-edge generative AI and real-world applications—crafting scalable ML solutions that turn creative potential into production reality.
🚀 Professional Experience:
- ML Solutions Architect at Brainchip Inc.: Optimizing generative AI systems end-to-end—from fine-tuning LLMs and building RAG pipelines to deploying multimodal voice solutions and guiding global clients through custom GenAI implementations.
- ML Solutions Architect Intern at Brainchip Inc.: Developed FastAPI web-based GUI tools, streamlined integrated circuit scaling, and automated unit testing with Pytest and Drone.
- Data Engineer at Walmart Global Tech: Led ETL mappings on GCP, optimized PySpark pipelines, and spearheaded data migration projects.
- Big Data Developer at Capgemini: Optimized Spark applications, designed data lakes, and pioneered automation using Azure Logic Functions and Azure Data Factory.
🎓 Education:
- MS in Computer Science & Engineering from University at Buffalo, The State University of New York. Specialized in Data Intensive Computing, Algorithms for Modern Computing Systems, and Machine Learning.
- BE in Computer Technology from Yeshwantrao Chavan College of Engineering.
🔧 Technical Stack:
- Programming Languages: Python, SQL, JavaScript, C/C++
- AI/ML Frameworks: PyTorch, HuggingFace, TensorFlow, Scikit-learn, FastAPI
- GenAI & ML Techniques: LLMs, RAG, Fine-tuning (LoRA/QLoRA), Transformers, Vector Databases, Prompt Engineering, CNN/RNN
- MLOps & Deployment: Docker, MLflow, AWS Bedrock, Azure ML, CI/CD, Model Registry
- Data & Cloud Platforms: AWS (S3, Lambda), GCP, Azure, Apache Spark, Kafka
- Orchestration: LangChain, LlamaIndex, Kubernetes
🌱 Currently Exploring: Sustainable and Responsible AI, Agentic Systems
Connect with me:
👇 Check out my repositories to see my hands-on projects and contributions!