Uday Paripelli

Results driven AI/ML Engineer with 4+ years of experience architecting and deploying production-scale machine learning and generative AI solutions across finance, healthcare, and enterprise domains. Proven success in building LLM-powered applications, RAG pipelines, fraud detection, and personalization systems that delivered double-digit efficiency gains, multimillion-dollar cost savings, and improved customer experiences. Skilled in Python, PySpark, SQL, and cloud platforms (AWS, Azure, GCP) with deep expertise in MLOps, CI/CD, Docker/Kubernetes, and model observability with MLflow.

Recognized for driving responsible AI adoption with a strong foundation in NLP, GenAI (GPT, LLaMA, LangChain), A/B testing, and time-series forecasting, consistently delivering compliant, scalable, and business-aligned AI solutions.

AI/ML Engineer - Morgan Stanley - USA

(2024-11)

• Architected LLM-powered agentic systems with LangChain, LangGraph, and CrewAI, enabling inter-agent memory and collaboration, improving workflow task completion by 30% across multiple financial operations.
• Fine-tuned LLMs(Hugging Face, LangChain) for clinical and legal summarization tasks, enhancing accuracy by 27% and reducing analyst review workload by 15 hours weekly.
• Deployed Generative AI solutions using AWS Bedrock (Claude, GPT-4) for document summarization and regulatory filings, achieving 45% faster processing with scalable automation.
• Optimized AWS SageMaker lifecycle configurations and right-sizing strategies, cutting compute costs by 18% (~$200K annually) without degrading performance.
• Designed and implemented RAG pipelines with Pinecone and FAISS, improving document retrieval accuracy by 35% and reducing manual lookup efforts, saving ~$400K yearly.
• Built and deployed a real-time fraud detection pipeline using XGBoost, PySpark, and SageMaker, achieving subsecond predictions, 42% higher detection accuracy, and 30% fewer false positives.
• Led Responsible AI initiatives by integrating fairness, bias detection, and explainability mechanisms into production pipelines ensuring SOC2 and HIPAA compliance.
• Developed an enterprise-grade multi-agent RAG Copilot using CrewAI, LangGraph, LangChain, and GPT-4, automating KYC/AML checks and regulatory workflows—boosting efficiency by 30% and saving ~$1.5M annually.

ML Engineer - GroovyWeb - India

(2020-05 - 2023-07)

• Conducted A/B testing of LLM prompts using LangChain and evaluation metrics (BLEU, ROUGE), improving chatbot response accuracy by 18% and customer satisfaction scores.
• Built deep learning models(CNN, RNN, LSTM, Transformers) for anomaly detection and predictive maintenance, achieving >90% accuracy and reducing MTTR by 25%.
• Automated ETL pipelines using Python, SQL, and SAS to streamline model ingestion, improving data throughput by 60% and reducing preparation time from days to hours.
• Developed scalable ML pipelines using PySpark, AWS Glue, and Redshift for real-time analytics, improving data delivery speed and business insights by 60%.
• Designed XGBoost-based failure prediction models that reduced system downtime by 15% and prevented approximately $1.2M in annual revenue loss.
• Implemented MLOps pipelines with Docker, Kubernetes, and MLflow to deploy and monitor financial models, achieving 99.9% uptime and reducing model release cycles by 35%.
• Built interactive dashboards and EDA visualizations (Matplotlib, Seaborn, Plotly, Power BI) uncovering customer behavior trends, improving reporting efficiency by 35%.
• Mentored engineers on LoRA fine-tuning, PEFT, and Responsible AI deployment practices ensuring GDPR and HIPAA compliance across enterprise AI initiatives.

Master's - Computer and Information Systems - Saint Louis University (2025-05)

Bachelor's - Electrical and Electronics Engineering - Malla Reddy Institute of Engineering and Technology (2022-07)