Rashmi Shetty

Built and leading the org tasked to deliver an Enterprise Generative AI Platform - focused on Agentic AI, RAG workflows, platform services (prompt designer/assembler, memory management, guardrails, semantic search, summarization, search platform, grounded agent provisioning..) ,Model compression, PEFT, Evaluation Harness, RHLF Org and Platform vision, roadmap, architecture to deliver GenAI workflows for use case based LLM agents via Compound AI Systems (agents, planner, tools, tasks, orchestration), With over 20 years of experience in building and delivering autoML platforms to deliver enterprise AI/ML products across diverse industries such as oil and gas, high tech, manufacturing, automotive, retail, IM&C, chemical.. I lead interdisciplinary teams to deliver an autoML platform catered to products for intelligent asset management. Metric based product vision, building and managing product roadmap with customer engagement. Experience in IoT and AI-based predictive maintenance solutions as co-innovation pilots. I drive team development, providing mentorship and technical advice on career progression, experimental designs, and machine learning pipelines. Part of my ethos includes engaging effectively with internal and external stakeholders, including C-level executives, to advocate for new technology solutions. I have a proven track record of producing comprehensive reports and documentation for both technical and non-technical audiences, facilitating clear communication across the board. I also foster the culture of continuous learning and innovation. Notably, my accomplishments include the design and implementation of novel solutions for automated failure mode detection in unstructured data and the creation of model-agnostic interpretation pipelines. I bring a strong skill set in R, SQL, Python, AI, AutoML, and IoT to bear in all aspects of my work. I bring in strong experience in data modeling, Feature engineering, MLOps, ML lifecycle workflows, quantitative analysis, Bayesian, anomaly detection, failure prediction, classification, clustering, tree-based models, time-series modeling, deep learning frameworks such as PyTorch and TensorFlow, Large language models, Generative AI - LLM fine tuning (PEFT - LoRA, QLoRA), student-teacher knowledge distillation, grounded multi-agent orchestration