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I am a Machine Learning Engineer with over 7 years of experience delivering scalable, data-driven solutions across healthcare, finance, and e-commerce. I design and deploy ML models using Python, TensorFlow, PyTorch, and scikit-learn, with a strong focus on end-to-end MLOps practices. I enjoy turning complex data challenges into production-ready solutions that create real business impact. I have built production-grade pipelines using MLflow, Docker, FastAPI, and AWS SageMaker, enabling seamless model lifecycle management and CI/CD automation. I work with large-scale data environments leveraging Spark, Hadoop, and Kafka for real-time analytics. My hands-on experience spans NLP and generative AI techniques, including BERT and GPT-based workflows, LangChain, and prompt engineering. I am deeply committed to model explainability and regulatory compliance, using SHAP and LIME to build trust in high-stakes decisions.…I am a Machine Learning Engineer with over 7 years of experience delivering scalable, data-driven solutions across healthcare, finance, and e-commerce. I design and deploy ML models using Python, TensorFlow, PyTorch, and scikit-learn, with a strong focus on end-to-end MLOps practices. I enjoy turning complex data challenges into production-ready solutions that create real business impact. I have built production-grade pipelines using MLflow, Docker, FastAPI, and AWS SageMaker, enabling seamless model lifecycle management and CI/CD automation. I work with large-scale data environments leveraging Spark, Hadoop, and Kafka for real-time analytics. My hands-on experience spans NLP and generative AI techniques, including BERT and GPT-based workflows, LangChain, and prompt engineering. I am deeply committed to model explainability and regulatory compliance, using SHAP and LIME to build trust in high-stakes decisions.

Uday Kumar Swamy

AI Engineer, Data Scientist, Developer, +1

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I am a Machine Learning Engineer with over 7 years of experience delivering scalable, data-driven solutions across healthcare, finance, and e-commerce. I design and deploy ML models using Python, TensorFlow, PyTorch, and scikit-learn, with a strong focus on end-to-end MLOps practices. I enjoy turning complex data challenges into production-ready solutions that create real business impact. I have built production-grade pipelines using MLflow, Docker, FastAPI, and AWS SageMaker, enabling seamless model lifecycle management and CI/CD automation. I work with large-scale data environments leveraging Spark, Hadoop, and Kafka for real-time analytics. My hands-on experience spans NLP and generative AI techniques, including BERT and GPT-based workflows, LangChain, and prompt engineering. I am deeply committed to model explainability and regulatory compliance, using SHAP and LIME to build trust in high-stakes decisions.…I am a Machine Learning Engineer with over 7 years of experience delivering scalable, data-driven solutions across healthcare, finance, and e-commerce. I design and deploy ML models using Python, TensorFlow, PyTorch, and scikit-learn, with a strong focus on end-to-end MLOps practices. I enjoy turning complex data challenges into production-ready solutions that create real business impact. I have built production-grade pipelines using MLflow, Docker, FastAPI, and AWS SageMaker, enabling seamless model lifecycle management and CI/CD automation. I work with large-scale data environments leveraging Spark, Hadoop, and Kafka for real-time analytics. My hands-on experience spans NLP and generative AI techniques, including BERT and GPT-based workflows, LangChain, and prompt engineering. I am deeply committed to model explainability and regulatory compliance, using SHAP and LIME to build trust in high-stakes decisions.

Available to hire

I am a Machine Learning Engineer with over 7 years of experience delivering scalable, data-driven solutions across healthcare, finance, and e-commerce. I design and deploy ML models using Python, TensorFlow, PyTorch, and scikit-learn, with a strong focus on end-to-end MLOps practices. I enjoy turning complex data challenges into production-ready solutions that create real business impact.

I have built production-grade pipelines using MLflow, Docker, FastAPI, and AWS SageMaker, enabling seamless model lifecycle management and CI/CD automation. I work with large-scale data environments leveraging Spark, Hadoop, and Kafka for real-time analytics. My hands-on experience spans NLP and generative AI techniques, including BERT and GPT-based workflows, LangChain, and prompt engineering. I am deeply committed to model explainability and regulatory compliance, using SHAP and LIME to build trust in high-stakes decisions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Language

English

Advanced

Work Experience

ML Engineer Intern at UnitedHealth Group

May 1, 2025 - August 21, 2025

Designed and implemented risk prediction models and NLP pipelines to improve healthcare forecasting and data extraction accuracy. Applied explainability techniques for compliance and trust, deployed models with FastAPI and Docker to reduce latency, and built ML pipelines with Airflow and AWS SageMaker Pipelines to enhance throughput. Utilized MLflow for model lifecycle management supporting cross-functional research and regulatory reviews.

Senior Machine Learning Engineer at Harman (A Samsung Company)

July 1, 2023 - August 21, 2025

Led machine learning projects for retail and financial sectors, delivering predictive models that reduced customer churn and marketing costs. Built spam detection classifiers and improved feature extraction via PCA. Tuned models with Bayesian Optimization and engineered CI/CD pipelines with Jenkins and AWS. Ensured GDPR compliance and improved model reliability and fairness through drift detection. Developed RESTful APIs with Spring Boot achieving low latency predictions.

Machine Learning Engineer at Cybage Software

June 1, 2016 - August 21, 2025

Developed recommendation engines increasing conversion rates and personalized marketing efforts. Reduced data preparation time and improved deployment reliability by containerizing models with Docker and AWS Lambda. Integrated SHAP dashboards for transparency and conducted A/B testing to enhance model accuracy and user satisfaction.

ML Engineer Intern at UnitedHealth Group

May 1, 2025 - September 5, 2025

Designed risk prediction models using Scikit-learn and XGBoost to improve patient-level forecasts and reduce false positives in internal healthcare review systems. Built NLP pipelines with Hugging Face Transformers and LangChain to extract unstructured clinical data. Applied SHAP and LIME to improve explainability and support HIPAA/compliance. Deployed models via FastAPI and Docker, reducing prediction latency and integrating into cloud-based patient reporting tools. Built ML pipelines with Airflow and AWS SageMaker Pipelines to boost throughput for large-scale healthcare datasets. Used MLflow for experiment tracking and lifecycle management across cross-functional teams and regulatory reviews.

Senior Machine Learning Engineer at Harman (A Samsung Company)

July 1, 2023 - September 5, 2025

Led design and development of ML solutions across 5 cross-functional teams in retail and financial services. Developed and deployed predictive models using Scikit-learn and PyTorch, achieving measurable improvements in churn reduction and customer engagement. Conducted market basket analysis and built a spam detection classifier to optimize marketing and communications. Implemented end-to-end CI/CD pipelines with Jenkins and AWS, and ensured GDPR-compliant data usage with drift detection and monitoring. Delivered scalable RESTful APIs for model inference with latency under 100ms and contributed to overall model reliability and fairness.

Machine Learning Engineer at Cybage Software

June 1, 2016 - September 5, 2025

Built recommendation engines using collaborative filtering and XGBoost to increase online conversions and personalize offers. Processed large datasets with SQL and Python, accelerating deployment of behavior prediction models. Deployed containerized models with Docker and AWS Lambda, enhancing uptime and reducing deployment overhead. Integrated SHAP-based dashboards to explain scores, and conducted A/B testing to improve prediction precision across product cycles.