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I am Rohith Velan Singaravelu, a dynamic Data Scientist and Machine Learning Engineer with over 4 years of experience designing and deploying end-to-end machine learning solutions. I specialize in Python, SQL, TensorFlow, Scikit-learn, and Pandas to extract insights and drive automation. I am skilled in deep learning, NLP, model optimization, and MLOps practices. I have a proven ability to build scalable pipelines and deploy models on AWS and Azure, delivering impactful, data-driven outcomes across diverse domains. Throughout my career, I have developed scalable ML pipelines, implemented custom fraud detection systems, created recommendation engines, and automated ETL workflows that significantly improved performance and efficiency in various enterprises. Passionate about continuous learning and collaboration, I thrive in cross-functional teams, fostering improvements and innovation in the adoption of ML technologies and MLOps standards.…I am Rohith Velan Singaravelu, a dynamic Data Scientist and Machine Learning Engineer with over 4 years of experience designing and deploying end-to-end machine learning solutions. I specialize in Python, SQL, TensorFlow, Scikit-learn, and Pandas to extract insights and drive automation. I am skilled in deep learning, NLP, model optimization, and MLOps practices. I have a proven ability to build scalable pipelines and deploy models on AWS and Azure, delivering impactful, data-driven outcomes across diverse domains. Throughout my career, I have developed scalable ML pipelines, implemented custom fraud detection systems, created recommendation engines, and automated ETL workflows that significantly improved performance and efficiency in various enterprises. Passionate about continuous learning and collaboration, I thrive in cross-functional teams, fostering improvements and innovation in the adoption of ML technologies and MLOps standards.

Rohith Velan Singaravelu

Data Scientist, AI Engineer, Developer, +1

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I am Rohith Velan Singaravelu, a dynamic Data Scientist and Machine Learning Engineer with over 4 years of experience designing and deploying end-to-end machine learning solutions. I specialize in Python, SQL, TensorFlow, Scikit-learn, and Pandas to extract insights and drive automation. I am skilled in deep learning, NLP, model optimization, and MLOps practices. I have a proven ability to build scalable pipelines and deploy models on AWS and Azure, delivering impactful, data-driven outcomes across diverse domains. Throughout my career, I have developed scalable ML pipelines, implemented custom fraud detection systems, created recommendation engines, and automated ETL workflows that significantly improved performance and efficiency in various enterprises. Passionate about continuous learning and collaboration, I thrive in cross-functional teams, fostering improvements and innovation in the adoption of ML technologies and MLOps standards.…I am Rohith Velan Singaravelu, a dynamic Data Scientist and Machine Learning Engineer with over 4 years of experience designing and deploying end-to-end machine learning solutions. I specialize in Python, SQL, TensorFlow, Scikit-learn, and Pandas to extract insights and drive automation. I am skilled in deep learning, NLP, model optimization, and MLOps practices. I have a proven ability to build scalable pipelines and deploy models on AWS and Azure, delivering impactful, data-driven outcomes across diverse domains. Throughout my career, I have developed scalable ML pipelines, implemented custom fraud detection systems, created recommendation engines, and automated ETL workflows that significantly improved performance and efficiency in various enterprises. Passionate about continuous learning and collaboration, I thrive in cross-functional teams, fostering improvements and innovation in the adoption of ML technologies and MLOps standards.

Available to hire

I am Rohith Velan Singaravelu, a dynamic Data Scientist and Machine Learning Engineer with over 4 years of experience designing and deploying end-to-end machine learning solutions. I specialize in Python, SQL, TensorFlow, Scikit-learn, and Pandas to extract insights and drive automation. I am skilled in deep learning, NLP, model optimization, and MLOps practices. I have a proven ability to build scalable pipelines and deploy models on AWS and Azure, delivering impactful, data-driven outcomes across diverse domains.

Throughout my career, I have developed scalable ML pipelines, implemented custom fraud detection systems, created recommendation engines, and automated ETL workflows that significantly improved performance and efficiency in various enterprises. Passionate about continuous learning and collaboration, I thrive in cross-functional teams, fostering improvements and innovation in the adoption of ML technologies and MLOps standards.

Skills

Experience Level

Expert

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Expert

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Intermediate

Work Experience

ML Engineer at Cardinal Health

December 1, 2024 - Present

Designed and deployed scalable machine learning pipelines using Airflow, DVC, and GenAI-based retraining triggers for over 50 annotation workflows, reducing model staleness and iteration lag by nearly 30%. Developed high-throughput LLM-powered NLP inference services using Hugging Face Transformers and TensorFlow Serving, containerized in Docker and deployed on Amazon ECS, enabling real-time processing with latency below 200ms. Built serverless batch scoring pipelines on AWS Lambda, delivering over 150K daily predictions while cutting compute usage by 23%. Established Kafka-based feedback loops for continuous human-in-the-loop model corrections, improving output reliability. Automated CI/CD pipelines with Jenkins and GitLab, enhancing deployment velocity by 57% and decreasing post-deployment incidents. Documented workflows and pipeline standards in Confluence to accelerate new engineer onboarding and strengthen collaboration.

Data Scientist at Deloitte

August 31, 2024 - August 26, 2025

Designed custom fraud detection systems using PyTorch, analyzing over 3 million monthly transactions and increasing anomaly detection precision by 18%. Constructed large-scale ETL workflows with Apache Spark, orchestrated using Azure Data Factory, processing over 5TB of regulatory data daily, accelerating reporting cycles. Standardized experimentation and model lifecycle workflows in Azure ML, reducing average model delivery time from 3 weeks to under 10 working days. Built and maintained operational dashboards in Power BI integrated with Azure SQL, enabling risk and compliance teams in 8 regions real-time metric access without analyst intervention. Managed model version control and parameter logging via MLflow, maintaining compliance-ready documentation for audit readiness. Collaborated cross-functionally translating policy rules into ML logic deployed in numerous enterprise-grade solutions.

Data Analyst at Myntra

August 31, 2022 - August 26, 2025

Built an NLP-based recommendation engine to enhance personalized product suggestions across 3-4 million monthly users, improving content relevance during peak seasons. Automated visual classification of over 500,000 fashion products using OpenCV and lightweight CNNs on AWS EC2, reducing manual cataloging effort. Conducted multi-week A/B tests on pricing strategies using campaign data processed with Pandas and stored in Amazon Redshift, increasing click-through rates by 12%. Developed interactive business dashboards in Tableau for marketing and merchandising, reducing reporting requests by 40%. Served prediction results via Flask API integrated into product and checkout flows for real-time insights. Analyzed over 30 million behavioral and transaction data rows with SQL and Pandas, driving segmentation for push campaigns yielding 50,000+ daily incremental interactions.

ML Engineer at Cardinal Health

December 1, 2024 - Present

Designed and deployed scalable ML pipelines with Airflow, DVC, and GenAI retraining triggers supporting 50+ annotation workflows. Developed high-throughput LLM-powered NLP inference services using Hugging Face Transformers and TensorFlow Serving, containerized via Docker and deployed on Amazon ECS with sub-200ms latency. Built serverless batch scoring pipelines on AWS Lambda for unstructured document processing delivering 150K+ daily predictions and reducing compute usage by 23%. Set up Kafka-based feedback loops for continuous learning with human-in-the-loop corrections. Automated CI/CD pipelines with Jenkins and GitLab to improve deployment velocity by 57% and reduce incidents. Documented model architectures, GenAI integration, and pipeline standards to accelerate engineer onboarding and cross-functional collaboration.

Data Scientist at Deloitte

August 1, 2024 - August 26, 2025

Designed custom fraud detection systems using PyTorch to analyze over 3 million transactions monthly, improving anomaly detection precision by 18%. Built large-scale ETL workflows with Apache Spark and Azure Data Factory, processing more than 5TB of regulatory data daily and accelerating reporting cycles. Standardized experimentation and model lifecycle workflows using Azure ML which reduced delivery times from 3 weeks to under two weeks. Created operational dashboards in Power BI integrated with Azure SQL, enabling real-time metrics access across eight regions. Managed model version control and logging via MLflow and maintained compliance-ready documentation. Collaborated cross-functionally to translate policy rules into ML logic deployed in multiple enterprise-grade solutions.

Data Analyst at Myntra

August 1, 2022 - August 26, 2025

Built NLP-based recommendation engine improving personalized product suggestions for 3-4 million monthly active users. Automated visual classification of over 500,000 fashion products using OpenCV and lightweight CNNs deployed on AWS EC2, reducing manual cataloging effort. Conducted A/B testing on pricing strategies leveraging campaign data stored in Amazon Redshift, increasing click-through rates by 12%. Developed interactive dashboards in Tableau for marketing and merchandising with real-time KPIs, reducing reporting requests by 40%. Served prediction results through Flask API integrated into product flows enabling real-time user insights. Analyzed 30+ million rows of transaction and behavioral data with SQL and Pandas to drive segmentation-based push campaigns yielding 50,000+ incremental daily app interactions.