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I’m Rathan Jayanath Singavarapu, an AI/ML engineer based in Jersey City, NJ, with 3+ years of experience in NLP, transformer models (BERT, ClinicalBERT), and LLM-based chatbots. I build ML pipelines, FastAPI services, and deploy models using Azure ML and Docker. My work spans medical imaging, explainable AI, and HIPAA-compliant data workflows. I thrive in cross-functional teams, applying SHAP and Captum for explainable AI, and I’m proficient with CI/CD automation, GitHub Actions, and Azure DevOps. I enjoy turning complex data into actionable insights and delivering end-to-end solutions from data ingestion to production monitoring.…I’m Rathan Jayanath Singavarapu, an AI/ML engineer based in Jersey City, NJ, with 3+ years of experience in NLP, transformer models (BERT, ClinicalBERT), and LLM-based chatbots. I build ML pipelines, FastAPI services, and deploy models using Azure ML and Docker. My work spans medical imaging, explainable AI, and HIPAA-compliant data workflows. I thrive in cross-functional teams, applying SHAP and Captum for explainable AI, and I’m proficient with CI/CD automation, GitHub Actions, and Azure DevOps. I enjoy turning complex data into actionable insights and delivering end-to-end solutions from data ingestion to production monitoring.

Rathan Jayanath Singavarapu

AI Engineer, Data Scientist, Data Analyst, +4

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I’m Rathan Jayanath Singavarapu, an AI/ML engineer based in Jersey City, NJ, with 3+ years of experience in NLP, transformer models (BERT, ClinicalBERT), and LLM-based chatbots. I build ML pipelines, FastAPI services, and deploy models using Azure ML and Docker. My work spans medical imaging, explainable AI, and HIPAA-compliant data workflows. I thrive in cross-functional teams, applying SHAP and Captum for explainable AI, and I’m proficient with CI/CD automation, GitHub Actions, and Azure DevOps. I enjoy turning complex data into actionable insights and delivering end-to-end solutions from data ingestion to production monitoring.…I’m Rathan Jayanath Singavarapu, an AI/ML engineer based in Jersey City, NJ, with 3+ years of experience in NLP, transformer models (BERT, ClinicalBERT), and LLM-based chatbots. I build ML pipelines, FastAPI services, and deploy models using Azure ML and Docker. My work spans medical imaging, explainable AI, and HIPAA-compliant data workflows. I thrive in cross-functional teams, applying SHAP and Captum for explainable AI, and I’m proficient with CI/CD automation, GitHub Actions, and Azure DevOps. I enjoy turning complex data into actionable insights and delivering end-to-end solutions from data ingestion to production monitoring.

Available to hire

I’m Rathan Jayanath Singavarapu, an AI/ML engineer based in Jersey City, NJ, with 3+ years of experience in NLP, transformer models (BERT, ClinicalBERT), and LLM-based chatbots. I build ML pipelines, FastAPI services, and deploy models using Azure ML and Docker. My work spans medical imaging, explainable AI, and HIPAA-compliant data workflows.

I thrive in cross-functional teams, applying SHAP and Captum for explainable AI, and I’m proficient with CI/CD automation, GitHub Actions, and Azure DevOps. I enjoy turning complex data into actionable insights and delivering end-to-end solutions from data ingestion to production monitoring.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/ML Engineer at CVS Health

March 1, 2025 - November 6, 2025

Built and deployed ML pipelines on Azure ML and FastAPI to enable patient scoring with lower latency and higher throughput by 40%. Fine-tuned ClinicalBERT and BioBERT for ICD classification and Q&A, achieving 85%+ F1 score on clinical datasets. Applied SHAP and Captum for explainable AI in healthcare use-cases supporting governance and FDA compliance. Engineered HIPAA-compliant pipelines with Azure Blob and Key Vault to ingest structured and unstructured EHR data. Developed LLM-based chatbot using Hugging Face Transformers and RAG for patient queries and report summarization. Automated model deployment cycles using CI/CD pipelines via GitHub Actions and Azure DevOps, reducing delivery time by 60%. Integrated monitoring tools and feedback loops to support continuous learning and error tracking in production models.

Associate Software Engineer – Databases at Teradata

August 31, 2023 - August 31, 2023

Embedded machine learning models within SQL engines to forecast workload spikes and optimize system resource allocation. Designed clustering workflows using K-Means and DBSCAN to identify slow query patterns and recommend automated resolutions. Trained ML models to suggest indexing strategies, improving query response times in high-throughput environments by 30%. Developed a lightweight Flask API layer for internal teams to visualize, monitor, and interpret predictive tuning insights. Created ETL workflows for log analytics and anomaly detection to alert on unexpected query behavior across distributed clusters.

Machine Learning Engineer Intern at Insight Global

May 31, 2022 - May 31, 2022

Cleaned and processed 200K+ data records for early-stage ML pipelines with Pandas and NumPy. Built ML models including logistic regression, decision trees, and random forests, achieving 75% accuracy on binary datasets. Engineered automated feature extraction and selection pipelines, reducing manual effort by 40%. Visualized model performance and feature importance using Matplotlib and Seaborn to support tuning and stakeholder demos. Contributed to agile sprint planning and backlog refinement; documented ML experiments and assisted in JIRA ticket resolutions.

Associate Software Engineer – Databases at Teradata

June 1, 2022 - August 1, 2023

Machine Learning Engineer Intern at Insight Global

August 1, 2021 - May 1, 2022

Cleaned and processed 200K+ data records for early-stage ML pipelines with Pandas and NumPy. Built ML models including logistic regression, decision trees, and random forests achieving 75% accuracy on binary datasets. Engineered automated feature extraction and selection pipelines, reducing manual effort by 40%. Visualized model performance and feature importance using Matplotlib and Seaborn to support tuning and stakeholder demos. Contributed to agile sprint planning and backlog refinement; documented ML experiments and assisted in Jira ticket resolutions.