# Harish Suresh Babu > Actively Seeking Opportunities | AI/ML Engineer at Humana | Machine Learning, Deep Learning & MLOps | Python, AWS, Azure | Healthcare AI & Production ML Systems Location: United States, United States Profile: https://flows.cv/harishsureshbabu AI/ML Engineer with 3+ years of experience building and deploying scalable machine learning and deep learning solutions in healthcare and enterprise environments. Skilled in Python, TensorFlow, PyTorch, SQL, PySpark, AWS, and Azure, with strong expertise in end-to-end ML pipelines, MLOps, and cloud deployment. Passionate about developing production-grade AI systems that drive measurable business and clinical impact. ## Work Experience ### AI/ML Engineer @ Humana Jan 2025 – Present • Designed and deployed end-to-end machine learning solutions using Python, TensorFlow, and Scikit-learn to predict patient risk, hospital readmissions, and care gaps, improving predictive accuracy by 32% and supporting proactive care interventions across large healthcare populations. • Built scalable data ingestion and preprocessing pipelines using PySpark, SQL, and AWS S3 to process multi-terabyte clinical, claims, and behavioral datasets, reducing data preparation time by 40% while ensuring high data quality and regulatory compliance. • Developed deep learning models including LSTM and transformer-based architectures for longitudinal patient data analysis, enabling early risk detection and reducing avoidable hospitalizations by 18% across monitored cohorts. • Implemented NLP pipelines using Python and TensorFlow to extract structured insights from unstructured clinical notes, improving documentation analysis accuracy by 29% and enhancing downstream clinical decision support systems. • Deployed machine learning models using Docker, Kubernetes, and AWS SageMaker, enabling automated scaling, version control, and rollback strategies that achieved 99.9% system uptime in production environments. • Collaborated with data engineering, clinical analytics, and business stakeholders to translate healthcare requirements into AI-driven solutions, accelerating model adoption and reducing decision latency by 35% across care management workflows. • Established MLOps workflows with CI/CD pipelines for continuous training, testing, and monitoring of models, reducing model drift incidents by 27% and ensuring consistent performance across production releases. • Conducted advanced feature engineering and hyperparameter optimization using grid search and Bayesian techniques, improving model precision and recall metrics by an average of 21% across multiple healthcare use cases. ### Machine Learning Engineer @ CitiusTech Jan 2021 – Jan 2024 • Developed machine learning models using Python, Scikit-learn, and XGBoost to support healthcare analytics initiatives, including cost prediction, utilization forecasting, and population health management, improving forecast accuracy by 26%. • Engineered robust ETL pipelines using PySpark, SQL, and cloud storage systems to integrate data from EHRs, claims systems, and external data sources, reducing manual data handling efforts by 45%. • Implemented deep learning models using TensorFlow and Keras for anomaly detection in healthcare transactions, reducing fraudulent or erroneous claim processing by 22% across client platforms. • Built NLP-based text classification and entity recognition solutions to analyze clinical documentation and physician notes, improving information extraction efficiency by 31% and supporting clinical quality reporting. • Deployed models into production using Dockerized microservices and REST APIs, enabling seamless integration with client applications and improving inference response times by 38%. • Collaborated with cross-functional teams including data scientists, healthcare SMEs, and QA engineers to ensure model outputs aligned with clinical and regulatory requirements, reducing post-deployment defects by 25%. • Optimized model performance through feature selection, dimensionality reduction, and tuning strategies, achieving consistent improvements in AUC and F1 scores across multiple projects. • Implemented automated model validation and monitoring frameworks to track performance metrics, data drift, and bias, improving long-term model stability and compliance readiness. • Created technical documentation and knowledge transfer materials to support client onboarding and internal teams, accelerating deployment cycles and reducing handover time by 30%. ## Education ### Masters in Data Science University at Buffalo ### Bachelor of Science in Mathematics Loyola College ## Contact & Social - LinkedIn: https://linkedin.com/in/harish-suresh-babu-ab966b212 --- Source: https://flows.cv/harishsureshbabu JSON Resume: https://flows.cv/harishsureshbabu/resume.json Last updated: 2026-04-16