# Naga Penugonda

> AI Scientist | ML Engineer | LLMs, Generative AI, Multi-Agent Systems, RAG, MLOps

Location: Harrison, New Jersey, United States
Profile: https://flows.cv/nagapenugonda

I am an AI Scientist and Machine Learning Engineer with 4 years of experience building intelligent systems using LLMs, transformer architectures, generative AI, multi-agent systems, and cloud-native ML workflows. My expertise spans NLP, deep learning, reinforcement learning, RAG pipelines, and MLOps, and I am passionate about developing scalable AI solutions that solve complex real-world problems across research and enterprise environments. 
 
Currently, I work at Google as an Applied AI Scientist, where I develop advanced AI systems for biomedical research. My work includes building multi-agent reasoning systems, training transformer-based models for protein interaction prediction and drug repurposing, designing GCP-based scalable ML infrastructure, and creating NLP-powered biomedical chatbots and literature intelligence systems that help accelerate scientific discovery. 
 
Before Google, I worked at HCL as a Machine Learning Engineer, where I developed predictive models for 5G network traffic and customer churn, built BERT-based NLP solutions for customer interaction analysis, and deployed scalable ML systems using AWS SageMaker, Docker, and CI/CD pipelines. This experience helped me build a strong foundation in production machine learning, real-time analytics, model monitoring, and responsible AI practices. 
 
I hold a Master of Science in Computer Science from California State University, San Bernardino. Combined with my professional experience, this has prepared me to contribute effectively to AI Engineer, Applied Scientist, Machine Learning Engineer, and Data Scientist roles where I can continue building impactful and innovative AI systems.

## Work Experience
### Applied AI Scientist @ Google
Jan 2024 – Present | California, United States
 Engineered a multi-agent AI system using JAX and TensorFlow to generate and validate biomedical hypotheses, integrating RAG pipelines over
40M+ genomic and chemical entries, reducing experimental search time from 48 to 31 hours per batch.
 Designed and trained transformer-based models for protein interaction prediction and drug repurposing using supervised and unsupervised
learning approaches, achieving a predictive accuracy increase of 22% on benchmark datasets.
 Developed a cloud-native architecture on Google Cloud Platform (GCP) leveraging Kubernetes for scalable model training and inference,
reducing end-to-end model deployment time by 40% and enabling continuous experimentation.
 Integrated SQL pipelines to efficiently preprocess and query multi-modal biomedical datasets, enabling rapid retrieval of structured and
unstructured data for downstream AI reasoning.
 Operationalized NLP-driven biomedical chatbots using transformer architectures and LangChain frameworks to provide researchers with
interactive, context-aware insights, accelerating literature search and experimental planning by 30%.
 Formulated and fine-tuned transformer models using Hugging Face for biomedical text understanding, enabling automated literature
summarization and hypothesis extraction, reducing research effort by 40% while maintaining 95% semantic accuracy across 10K+ articles.
 Conducted end-to-end R&D on multi-agent reasoning systems that combine supervised learning, unsupervised clustering, and reinforcement
learning to simulate complex biological interactions, contributing to three peer-reviewed publications in computational biology.
 Optimized AI model performance and resource utilization on GCP by leveraging distributed training, mixed-precision computation, and realtime monitoring dashboards, improving inference efficiency by 25% while maintaining model accuracy on large-scale biomedical datasets.

### Machine Learning Engineer @ HCL
Jan 2021 – Jan 2023 | India
 Programmed and deployed predictive models for 5G network traffic and customer churn using Python, PyTorch, and ensemble methods
(Random Forest, XGBoost, LightGBM), achieving 92% accuracy and reducing network downtime forecasting errors by 28%.
 Devised and fine-tuned BERT-based NLP pipelines for analyzing telecom customer interactions, leveraging LLM embeddings to classify service
requests, improving automated ticket resolution by 35% while reducing manual triage workload.
 Implemented end-to-end CI/CD pipelines using Docker and AWS SageMaker for scalable model training and deployment, reducing model
release cycle time from 14 days to 5 days across multiple environments.
 Built real-time analytics pipelines on AWS Cloud integrating MongoDB and time-series forecasting models to predict network load and optimize
5G bandwidth allocation, processing 2M+ traffic events daily, improving network utilization by 18%.
 Performed hyperparameter optimization using Optuna and Ray Tune for deep learning and ensemble models, boosting predictive model F1
scores by 12% and ensuring robustness across diverse telecom datasets
 Established Responsible AI practices, including bias detection, fairness evaluation, and model explainability in production pipelines, ensuring
compliance with regulatory standards while maintaining high model performance across telecom and biomedical datasets.
 Architected comprehensive model validation and unit testing frameworks to ensure production reliability, including statistical performance
monitoring, drift detection, and reproducibility of results in cloud deployments.
 Collaborated with cross-functional teams to integrate ML models with telecom APIs, enabling personalized service recommendations and
automated analytics dashboards, driving a 22% increase in customer engagement and revenue optimization.


## Education
### Master's Degree in Computer Science
California State University-San Bernardino

### Bachelor's Degree in Computer Science
R.V.R. & J.C. College of Engineering


## Contact & Social
- LinkedIn: https://linkedin.com/in/naga-penugonda-24b5bb240

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Source: https://flows.cv/nagapenugonda
JSON Resume: https://flows.cv/nagapenugonda/resume.json
Last updated: 2026-04-17