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Bhuvana Chandrika Natharga

@bhuvanachandrikanath

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AI/ML Engineer; I build production LLM, fraud, and computer-vision systems with MLOps.

United States

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What I'm looking for

I want to build and productionize impactful AI—especially LLMs/RAG, fraud, and computer vision—using strong MLOps (MLflow, Docker, Kubernetes, CI/CD) and scalable data pipelines. I value fast iteration, reliability, and measurable business impact.

I’m an AI/ML Engineer with 6+ years of experience designing, building, and deploying scalable Machine Learning, Deep Learning, and Generative AI solutions. I’ve delivered production-grade systems across financial services and medical-device domains, with a focus on predictive analytics, intelligent automation, and document intelligence.

At Beal Bank, I develop fraud detection and credit risk scoring models that strengthen real-time transaction monitoring and reduce false positives by 22%. I also build LLM-powered document intelligence and internal chatbot solutions using GPT, LangChain, and Hugging Face, including RAG-based enterprise search over loan, policy, and compliance documents.

In the medical-device space at Alcon, I improved ophthalmic defect detection accuracy by 17% using Python, PyTorch, and XGBoost. I’ve also created real-time inference services with low-latency FastAPI, Docker, and Kubernetes, and supported forecasting and supply-planning with Prophet and NeuralForecast.

Previously at Goldman Sachs and CommScope, I built fraud detection and customer analytics models, analyzed millions of records, and improved customer segmentation effectiveness by 20%. Across roles, I prioritize reliable MLOps pipelines with MLflow, Kubeflow, Docker, Kubernetes, and CI/CD, plus scalable data engineering using Apache Spark, Databricks, Kafka, and Airflow.

Experience

Work history, roles, and key accomplishments

Current

AI/ML Engineer

Current

Beal Bank

Jun 2025 - Present (1 year)

Built fraud detection and credit risk scoring models that reduced false positives by 22% and strengthened real-time transaction monitoring. Developed LLM-powered document intelligence and RAG enterprise search, and deployed low-latency inference APIs with production MLOps using MLflow and Kubernetes.

Python XGBoost LightGBM LLM RAG fastAPI Docker Kubernetes MLFlow Pyspark

Machine Learning Engineer

Alcon

Aug 2024 - May 2025 (9 months)

Developed deep learning models for ophthalmic image analysis and quality inspection, improving defect detection accuracy by 17%. Built real-time inference services and LLM-powered document intelligence, and engineered scalable data pipelines for production model delivery.

Python PyTorch XGBoost MLFlow Airflow Optuna fastAPI Transformers Pyspark

Data Scientist

Goldman Sachs

Jan 2022 - Jul 2023 (1 year 6 months)

Built fraud detection and customer analytics models to enable proactive risk management across financial products. Developed scalable ETL pipelines with Airflow/AWS Glue and delivered BI dashboards that improved customer segmentation effectiveness by 20%.

scikit learn XGBoost LightGBM Python SQL Pandas Apache Spark Apache Airflow Tableau Power BI

Data Scientist

CommScope

May 2019 - Dec 2021 (2 years 7 months)

Developed ML models for predictive maintenance and demand forecasting to support proactive network operations. Built anomaly detection and scalable data pipelines, and delivered executive dashboards for real-time network performance visibility.

XGBoost Anomaly Detection Python SQL Pyspark Databricks Tableau Power BI

Education

Degrees, certifications, and relevant coursework

Illinois Institute of Technology

Master of Science in Data Science, Data Science

2023 - 2025

Grade: 3.45/4.0

Pursuing an M.S. in Data Science, maintaining a GPA of 3.45/4.0. Coursework focused on data science fundamentals and applied analytics.

CVR College of Engineering

Bachelor of Technology in Electrical and Electronics Engineering, Electrical and Electronics Engineering

2017 - 2021

Grade: 3.5/4.0

Completed a Bachelor of Technology in Electrical and Electronics Engineering with a GPA of 3.5/4.0. Built a foundation in core electrical engineering concepts and engineering problem-solving.