Pranay RishithBondugula
I build end-to-end intelligent systems — from raw data pipelines and model training to production deployment and monitoring — across the full ML lifecycle.
Building with Data,
End to End
I'm a Data Scientist with 4 years of hands-on experience across the full AI/ML lifecycle — from collecting and transforming raw data, to training and evaluating models, to shipping them into production where they handle real workloads.
At Accenture, I worked on GenAI systems powered by large language models — designing retrieval systems, running experiments to improve output quality, and monitoring their behavior in production. At Harman International, I built data pipelines and ML models that processed high-volume sensor data and produced real-time predictions across a large fleet of connected devices.
I'm most effective when I can move across the problem — working with data, building models, and making sure those models actually run and stay healthy in production. I hold an M.S. in Data Science from the University of North Texas.
Data & Analysis
- Large-scale data pipelines
- Feature engineering
- Statistical modeling
- A/B experimentation
- EDA & visualization
Modeling & AI
- Supervised & unsupervised ML
- Deep learning & NLP
- GenAI & LLMs
- RAG & fine-tuning
- Model evaluation & iteration
Deployment & Scale
- Production ML systems
- Model monitoring & reliability
- API development
- Cloud infrastructure
- End-to-end ML lifecycle
Where I've Done the Work
4 years across two production environments — spanning data, modeling, and deployment.
AI / ML Engineer
Accenture
- Designed and deployed a GenAI system using LLMs and retrieval-augmented generation, serving large volumes of user queries in production
- Applied prompt engineering, fine-tuning, and evaluation frameworks to improve model output quality and reliability
- Collaborated on the full model lifecycle — from data preparation and experimentation to deployment and performance monitoring
- Conducted experimentation and analysis to measure the impact of system changes on end-user outcomes
M.S. Data Science
University of North Texas
- Graduate program covering machine learning, statistical modeling, distributed systems, and applied AI
Data Scientist
Harman International
- Built scalable data pipelines to process high-volume, real-time sensor telemetry from a large fleet of connected devices
- Developed and evaluated machine learning models for anomaly detection, achieving significant accuracy improvements through iterative experimentation
- Performed feature engineering, exploratory data analysis, and model selection across structured and time-series datasets
- Optimized and deployed trained models into production environments, ensuring reliability and performance at scale
Technical Toolkit
A consolidated view of the tools and frameworks I use across the ML lifecycle.
Machine Learning & AI
Model training, deep learning, and generative AI pipelines.
Data & Analytics
Processing high-volume streaming and batch data at scale.
MLOps & Infrastructure
Containerization, orchestration, and model deployment.
Selected Work
A sample of projects — from data pipelines and model training to production AI systems.
Legal Document RAG System
IoT Anomaly Detection at Scale
AI Agent for Multi-Step Reasoning
Quick Thoughts
Bite-sized notes on engineering, machine learning pipelines, and scale.
Day 16: Explaining ML's Neglected Concepts - 𝗕𝗮𝘁𝗰𝗵 𝘃𝘀. 𝗦𝘁𝗿𝗲𝗮𝗺 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴
Most tutorials teach you batch. Most jobs eventually need stream. They're not just different speeds. They're different assumptions about when data shows up.
Read short →Day 15: Explaining ML's Neglected Concepts - 𝗢𝗟𝗔𝗣
The reason your "fast" database still can't answer a simple analytics question. You trained the model. The pipeline runs. Then someone asks a question and your stack chokes.
Read short →Get In Touch
pranay@portfolio:~$ get_in_touch Available channels: email → pranayrishith.usa@gmail.com github → github.com/pranayrishith16 linkedin → linkedin.com/in/pranayrishith Type 'help' for commands.