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I am a results-driven AI/ML Engineer specializing in production-grade ML systems for healthcare and enterprise. I design scalable ML architectures, deploy models in cloud-native MLOps pipelines across AWS, Azure, and GCP, and optimize inference latency to deliver actionable predictive analytics. I thrive on turning raw healthcare data into deployable AI solutions that lower costs, improve accuracy, and enhance patient outcomes. I'm passionate about bridging applied AI with large-scale data infrastructure and building explainable, compliant systems that deliver measurable business impact.…I am a results-driven AI/ML Engineer specializing in production-grade ML systems for healthcare and enterprise. I design scalable ML architectures, deploy models in cloud-native MLOps pipelines across AWS, Azure, and GCP, and optimize inference latency to deliver actionable predictive analytics. I thrive on turning raw healthcare data into deployable AI solutions that lower costs, improve accuracy, and enhance patient outcomes. I'm passionate about bridging applied AI with large-scale data infrastructure and building explainable, compliant systems that deliver measurable business impact.

Deeksha Mallampet

Data Scientist, AI Engineer, AI Developer, +2

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I am a results-driven AI/ML Engineer specializing in production-grade ML systems for healthcare and enterprise. I design scalable ML architectures, deploy models in cloud-native MLOps pipelines across AWS, Azure, and GCP, and optimize inference latency to deliver actionable predictive analytics. I thrive on turning raw healthcare data into deployable AI solutions that lower costs, improve accuracy, and enhance patient outcomes. I'm passionate about bridging applied AI with large-scale data infrastructure and building explainable, compliant systems that deliver measurable business impact.…I am a results-driven AI/ML Engineer specializing in production-grade ML systems for healthcare and enterprise. I design scalable ML architectures, deploy models in cloud-native MLOps pipelines across AWS, Azure, and GCP, and optimize inference latency to deliver actionable predictive analytics. I thrive on turning raw healthcare data into deployable AI solutions that lower costs, improve accuracy, and enhance patient outcomes. I'm passionate about bridging applied AI with large-scale data infrastructure and building explainable, compliant systems that deliver measurable business impact.

Available to hire

I am a results-driven AI/ML Engineer specializing in production-grade ML systems for healthcare and enterprise. I design scalable ML architectures, deploy models in cloud-native MLOps pipelines across AWS, Azure, and GCP, and optimize inference latency to deliver actionable predictive analytics.

I thrive on turning raw healthcare data into deployable AI solutions that lower costs, improve accuracy, and enhance patient outcomes. I’m passionate about bridging applied AI with large-scale data infrastructure and building explainable, compliant systems that deliver measurable business impact.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Language

English

Fluent

Work Experience

AI/ML Engineer at Cigna

January 1, 2025 - November 5, 2025

Architected real-time ML pipelines integrating AWS SageMaker, Lambda, and EC2 to automate healthcare risk stratification, reducing model inference time by 45%. Engineered modular training pipelines (PyTorch + MLflow) supporting distributed hyperparameter optimization across multi-node GPU clusters. Developed automated retraining pipelines with Airflow and DVC for continuous learning from streaming EHR data, improving prediction stability by 28%. Containerized AI workloads using Docker and deployed inference services via Kubernetes + Kubeflow, achieving 99.5% uptime and horizontal scaling. Implemented model version control and experiment tracking through MLflow, ensuring reproducibility and auditable lineage for all deployments. Collaborated with clinicians to integrate explainable AI models into internal decision engines, improving patient engagement metrics by 22%. Ensured HIPAA-compliant security layers with zero PHI leakage during training.

Machine Learning Engineer at Neon IT Systems

July 1, 2022 - July 1, 2022

Designed and deployed end-to-end ML pipelines using PySpark and Snowflake for real-time claims prediction and fraud detection across 10M+ records. Built and optimized CNN-LSTM architectures for pattern recognition in healthcare imaging data, increasing detection precision by 18%. Developed streaming ingestion frameworks (Kafka + Azure Functions) for real-time event capture and online inference, reducing processing latency by 30%. Automated deployment via Jenkins and CI/CD for ML workflows, enabling zero-downtime model updates. Introduced feature store architecture for offline/online feature parity, decreasing feature drift by 25%. Created real-time dashboards in Streamlit and Plotly to visualize drift, performance metrics, and explainability outputs in real time.