Bharadwaja O
Ai Ml Engineer @Flatiron Health
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WORK HISTORY
Ai Ml Engineer @Flatiron Health
US
Processe large oncology datasets using Azure ML and Azure Databricks, handling over patient records and saving approximately1,500 manual annotation hours annually. Train transformer-based NLP models with Hugging Face and PyTorch to extract clinical entities, reducing around annotationerrors per quarter and improving patient-level data quality. Automate ML workflows using Apache Airflow and GitHub Actions, supporting generative model experiments with Diffusers andcutting retraining and deployment time by nearly 40 hours per week across multiple clinical projects. Fine-tune LLMs using Lo RA and deploy containerized inference pipelines with Docker and Kubernetes, keeping clinical AI pipelinesrunning at 99.7% uptime. Displaymodel explain ability outputsfrom SHAP and LIME in Power BI dashboards, enabling clinical teamsto review and verify predictions per month. Annotate oncology imaging datasets using computer vision pipelines with YOLOv8 and Detectron2, labeling over images andaccelerating model deployment cycles. Assess LLMs (GPT-4, Claude, Mistral) with Cursor AI and LangChain, reducing inference costs by approximately $150K annually andimproving clinical documentation relevance for more than 2,500 repor
EDUCATION
Sathyabama Institute of Science & Technology, Chennai
Bachelor's degree, Electrical, Electronics and Communications Engineering
University of Wisconsin-Milwaukee
Master of Science - MS, Information Technology Management
ABOUT BHARADWAJA O
AI/ML Engineer with 4+ years of experience building and deploying machine learning models and automation pipelines in healthcare and enterprise environments. Proficient in advanced data preprocessing, feature engineering, model development, and hyperparameter tuning, with hands-on expertise in Python, TensorFlow, and PyTorch for constructing robust ML pipelines. Skilled at leveraging cloud platforms (AWS, Azure) and implementing MLOps practices to deliver reliable production models. Experienced in applying machine learning to clinical and patient datasets, creating predictive analytics solutions, and presenting insights through data visualization, while collaborating effectively with cross-functional teams in Agile environments.
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