# Bringesh Chowdavarapu

> AI/ML Engineer | 4+ Years Experience | NLP • Transformers • MLOps | AWS • Healthcare & Enterprise ML

Location: San Jose, California, United States
Profile: https://flows.cv/bringesh

AI/ML Engineer with 4+ years of experience designing, deploying, and scaling machine learning solutions across healthcare and enterprise environments. I specialize in taking models from experimentation to production-ready systems that are reliable, compliant, and business-aligned.

My work spans NLP, transformer models (BERT, LLMs), predictive analytics, and end-to-end MLOps, with hands-on experience building cloud-native ML pipelines on AWS. I’ve delivered measurable impact, including 28% improvements in data reliability, 35% reduction in manual review effort, and 31% gains in predictive accuracy.

At Johnson & Johnson, I design and deploy production ML pipelines, build transformer-based NLP solutions for clinical text analysis, and implement robust MLOps workflows using Docker, MLflow, CI/CD, and real-time model serving with FastAPI. I collaborate closely with data science, product, and regulatory teams to ensure solutions meet both technical and compliance requirements in regulated environments.

Previously, I worked on predictive modeling, recommender systems, time-series forecasting, NLP, and computer vision solutions, delivering scalable APIs and analytics systems that directly supported business decision-making.

Core strengths:

• Machine Learning & Deep Learning (NLP, Transformers, CV, Time Series)
• Production ML & MLOps (Docker, Kubernetes, MLflow, CI/CD, Monitoring)
• Cloud ML (AWS, GCP, Azure)
• Python-based ML development & API deployment
• Translating complex data problems into real-world AI solutions

I’m passionate about building scalable, explainable, and high-impact ML systems and continuously learning in fast-evolving AI spaces.

## Work Experience
### AI/ML Engineer @ Johnson & Johnson
Jan 2025 – Present
• Designed and deployed production ML pipelines for healthcare analytics using Python, Pandas, NumPy, and scikit-learn, improving model reliability and downstream data quality by 28%.
• Built and fine-tuned transformer-based NLP models for clinical text classification and entity extraction using BERT architectures, reducing manual review effort for research teams by 35%.
• Implemented end-to-end MLOps workflows using Docker, MLflow, and GitHub Actions, standardizing model versioning, experiment tracking, and CI pipelines across teams.
• Deployed real-time inference services using FastAPI and REST APIs on AWS EC2 and S3, enabling scalable model serving with consistent latency under production workloads (22% latency reduction).
• Collaborated with data science, regulatory, and product stakeholders to translate clinical and business requirements into deployable ML solutions, ensuring auditability and compliance readiness.
• Integrated monitoring and alerting using Prometheus and custom logging to track data drift, model performance, and service health, improving incident detection without disrupting clinical operations.

### Machine Learning Engineer @ Infinite Infolab
Jan 2020 – Jan 2023
• Developed supervised and unsupervised ML models for predictive analytics and recommender systems using Python, scikit-learn, XGBoost, and LightGBM, increasing client KPI accuracy by 31%.
• Designed time-series forecasting solutions using statistical models and deep learning approaches, enabling demand and trend prediction that improved planning decisions by 24%.
• Built NLP pipelines for sentiment analysis, text classification, and named entity recognition using transformers and traditional NLP techniques, supporting data-driven product insights for multiple clients.
• Implemented computer vision models for image classification and object detection using CNN architectures, delivering automated quality checks and visual analysis workflows.
• Deployed ML models as scalable APIs using Flask and FastAPI, integrating with client applications through REST interfaces and ensuring production readiness.
• Established model experimentation, tracking, and reproducibility practices using MLflow and structured Git workflows, reducing rework and model regression issues by 27%.
• Partnered with cross-functional teams to perform data validation, feature engineering, and exploratory analysis using SQL, Pandas, and visualization tools, translating raw data into actionable insights.


## Education
### Master's degree in Statistics
California State University - East Bay

### Bachelor of Technology - BTech in electronics and communication engineering
Amrita Vishwa Vidyapeetham


## Contact & Social
- LinkedIn: https://linkedin.com/in/bringesh-chowdavarapu
- GitHub: https://github.com/bringesh2001

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