Hi, I'm Praneeth Devunuri
Senior AI Scientist @ Optym
I describe myself as the ε-guy. In reinforcement learning, the epsilon value represents exploration vs exploitation. I embrace this policy by constantly learning and applying complex algorithmic models to automate and optimize the physical and digital worlds. Transitioned from PhD research in Connected and Autonomous Vehicles (UIUC) to driving next-generation AI and optimization systems at Optym.
Professional & Academic Affiliations
Technical Expertise & Core Focus
A unique synthesis of theoretical reinforcement learning, computer vision, and hands-on LLM engineering.
Generative AI & LLMs
- Agentic Workflows (crewAI, LangGraph)
- Hybrid RAG & Semantic Search
- LLM Fine-tuning (LoRA, PEFT)
- Inference Optimization (vLLM)
Reinforcement Learning
- Q-Learning & Policy Gradients
- Autonomous Control Algorithms
- Simulation Modeling (SUMO, VISSIM)
- Decision Processes (MDPs)
Computer Vision
- Object Detection & Tracking (YOLO)
- Image Processing (OpenCV)
- Spatial-CNN (Lane Line Prediction)
- Sensor Fusion (Drones & Vehicles)
Featured Generative AI Work
Showcasing current projects built in the LLM and multi-agent space.
Agentic RAG System for Technical Documentation
An enterprise-grade Retrieval-Augmented Generation (RAG) system engineered for high-accuracy question answering over complex developer documentation and multi-page PDFs.
Read full detailsFine-Tuning Code Llama with PEFT/LoRA
A specialized code-generation assistant developed by fine-tuning Code Llama (13B) on domain-specific repository patterns, improving codebase-specific code completions by 35%.
Read full detailsMulti-Agent Software Engineering Workspace
An autonomous agent workspace where a crew of specialized AI agents (Product Manager, Coder, QA Engineer, Tech Writer) collaborate to plan, write, test, and write documentation for small software features.
Read full detailsProfessional Journey
A timeline of my academic background and engineering transition.
Senior AI Scientist
Optym
Developing and deploying state-of-the-art machine learning models, optimization algorithms, and Generative AI (LLM) workflows to solve complex logistics and transportation scheduling challenges.
Ph.D. Researcher & Doctoral Student
University of Illinois Urbana-Champaign
Researched intelligent transportation systems, reinforcement learning for signal controls, and Large Language Model (LLM) frameworks for transit data under Dr. Lewis Lehe. Graduated in 2025.
Graduate Assistant Researcher
Texas A&M University
Built autonomous ride-sharing simulations, modeled pricing schemes for Mobility-as-a-Service, and performed emergency vehicle response experiments.
Bachelor of Technology
Indian Institute of Technology, Madras
Developed image processing frameworks for traffic extraction, created glass fragmentation test prototypes, and served as Webops Head.