I build intelligent solutions that harness the power of Large Language Models (LLMs) to unlock the full potential of enterprise data. With over 14 years of experience in information technology, I'm a driven AI Engineer passionate about creating cutting-edge AI solutions. My mission is to bridge the gap between human language and machine intelligence, transforming complex data into actionable insights that drive business value.My journey began with a solid foundation in software development and database management at Infosys and Oracle. At Capgemini, I delved into data-driven decision-making through data warehousing, reporting, and dashboard development. Equipped with a Master's in Data Science, I've further sharpened my skills in data engineering, machine learning, and NLP at SMP and ePlus.Notable Achievements:• RAG-Based Chatbot: Designed and implemented a Retrieval Augmented Generation (RAG) chatbot on an on-prem NVIDIA stack. This solution leverages enterprise knowledge bases to provide highly accurate responses, projected to reduce manual research time by 20%.• Fine-tuned LLMs: Leveraged fine-tuning techniques on large language models using proprietary enterprise data, enhancing their understanding of domain-specific terminology and improving report generation.• Robust Data Pipelines: Built a robust data pipeline and architected a data warehouse that seamlessly integrates with internal systems and vendor APIs, ensuring 100% fulfillment of the customer lifecycle.Beyond work, I actively collaborate with our company's Wellness Director on charitable events. I'm also an avid Pickleball player and find balance through running, yoga, and meditation.Key Skills & Technologies:• AI Engineering: LLMs (GPT-4, Llama, Mistral), Generative AI, RAG Models (Langchain, LlamaIndex), Fine-tuning (LoRA, NVIDIA Nemo), Chatbot Development, Prompt Engineering, Vector Databases (Pinecone, Weaviate, Milvus), Embedding (Huggingface TEI), Inferencing (NVIDIA Triton, vLLM), NVIDIA Guardrails, App Development (FastAPI, Streamlit), Deployment (Docker), Monitoring (Prometheus, Grafana)• Data Science: Machine Learning (Scikit-learn, XGBoost, PyTorch), NLP (Transformers, spaCy, NLTK), Data Analysis (Pandas, NumPy)• Data Engineering: Spark, Data Pipelines (Airflow, Azure Data Factory), Data Lakes, Data Warehousing (Snowflake, Azure Synapse)• Cloud Technologies: AWS, Azure• Programming: Python, R, Go, Java, Scala, Node.js, SQL
Listed skills include Qtp, Oracle Fusion, Oracle Applications, Test Planning, and 32 others.