AI Engineer · Tempe, Arizona
Building production-grade Generative AI: RAG pipelines, multi-agent architectures, and real-time ML systems that scale.
// about me
I'm an AI Engineer pursuing my Master's in Computer Science at Arizona State University (GPA 3.89), specializing in the full stack of modern Generative AI, from vector retrieval and agent reasoning to cloud deployment and observability.
My work spans multi-modal emotion recognition, agentic voice pipelines, MLOps platforms, and RAG hallucination detection. I care deeply about systems that are reliable, measurable, and actually ship.
Currently building at ASU's RVCoLab MIX Center, where I design GenAI pipelines that power AI-driven virtual avatars in real time.
// technical skills
// experience
// projects
Full-stack MLOps platform for personalized habit optimization. Contextual multi-armed bandit system (Thompson Sampling) using 14 behavioral features over 30-day windows to dynamically select personalized interventions.
Low-latency RAG validation system achieving <162ms retrieval across 198 research papers. Multi-stage hallucination detection pipeline (entropy + divergence + NLI) with evaluation dashboards for systematic debugging.
Multi-agent LLM system with ReAct-based reasoning loops, tool integration (search, code execution, file I/O), and persistent vector memory for multi-session context. Reduced manual review effort by 30–40%.
Benchmarked 5 deep learning architectures on 100K+ audio samples, achieving 92.1% accuracy with 12ms inference latency. Robust MFCC preprocessing pipeline for generalization across noisy environments.
MobileNetV2 trained on 12K dermoscopy images across 8 disease classes with 95% diagnostic accuracy. Co-authored a granted patent (IND 102069, Sept 2022). MC Dropout for uncertainty estimation improved generalizability by 18%.
// education
// contact
Open to AI/ML engineering roles, research collaborations, and interesting conversations about agentic systems.
sgudiva3@asu.edu