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I am Kavya Ananthula, an AI/ML Engineer with 5+ years of experience designing, deploying, and operating production-grade ML and Generative AI systems. I specialize in Python, PyTorch, TensorFlow, NLP, LLMs, and Retrieval-Augmented Generation (RAG). I own the full ML lifecycle—from data pipelines and model training to cloud deployment and monitoring—and I build scalable, low-latency AI solutions using Docker, Kubernetes, CI/CD, and multiple cloud platforms. I am adept at MLOps, LLM Ops, experiment tracking, drift detection, and model optimization, ensuring performance, reliability, and compliance in regulated environments. I thrive in cross-functional teams with clinicians, data scientists, and product owners, translating complex healthcare requirements into practical AI solutions while upholding Responsible AI practices and governance.…I am Kavya Ananthula, an AI/ML Engineer with 5+ years of experience designing, deploying, and operating production-grade ML and Generative AI systems. I specialize in Python, PyTorch, TensorFlow, NLP, LLMs, and Retrieval-Augmented Generation (RAG). I own the full ML lifecycle—from data pipelines and model training to cloud deployment and monitoring—and I build scalable, low-latency AI solutions using Docker, Kubernetes, CI/CD, and multiple cloud platforms. I am adept at MLOps, LLM Ops, experiment tracking, drift detection, and model optimization, ensuring performance, reliability, and compliance in regulated environments. I thrive in cross-functional teams with clinicians, data scientists, and product owners, translating complex healthcare requirements into practical AI solutions while upholding Responsible AI practices and governance.

Kavya Ananthula

AI Engineer, AI Developer, Data Scientist, +2

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I am Kavya Ananthula, an AI/ML Engineer with 5+ years of experience designing, deploying, and operating production-grade ML and Generative AI systems. I specialize in Python, PyTorch, TensorFlow, NLP, LLMs, and Retrieval-Augmented Generation (RAG). I own the full ML lifecycle—from data pipelines and model training to cloud deployment and monitoring—and I build scalable, low-latency AI solutions using Docker, Kubernetes, CI/CD, and multiple cloud platforms. I am adept at MLOps, LLM Ops, experiment tracking, drift detection, and model optimization, ensuring performance, reliability, and compliance in regulated environments. I thrive in cross-functional teams with clinicians, data scientists, and product owners, translating complex healthcare requirements into practical AI solutions while upholding Responsible AI practices and governance.…I am Kavya Ananthula, an AI/ML Engineer with 5+ years of experience designing, deploying, and operating production-grade ML and Generative AI systems. I specialize in Python, PyTorch, TensorFlow, NLP, LLMs, and Retrieval-Augmented Generation (RAG). I own the full ML lifecycle—from data pipelines and model training to cloud deployment and monitoring—and I build scalable, low-latency AI solutions using Docker, Kubernetes, CI/CD, and multiple cloud platforms. I am adept at MLOps, LLM Ops, experiment tracking, drift detection, and model optimization, ensuring performance, reliability, and compliance in regulated environments. I thrive in cross-functional teams with clinicians, data scientists, and product owners, translating complex healthcare requirements into practical AI solutions while upholding Responsible AI practices and governance.

Available to hire

I am Kavya Ananthula, an AI/ML Engineer with 5+ years of experience designing, deploying, and operating production-grade ML and Generative AI systems. I specialize in Python, PyTorch, TensorFlow, NLP, LLMs, and Retrieval-Augmented Generation (RAG). I own the full ML lifecycle—from data pipelines and model training to cloud deployment and monitoring—and I build scalable, low-latency AI solutions using Docker, Kubernetes, CI/CD, and multiple cloud platforms.

I am adept at MLOps, LLM Ops, experiment tracking, drift detection, and model optimization, ensuring performance, reliability, and compliance in regulated environments. I thrive in cross-functional teams with clinicians, data scientists, and product owners, translating complex healthcare requirements into practical AI solutions while upholding Responsible AI practices and governance.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

AI/ML Engineer at Optum

December 1, 2024 - Present

Designed and productionized healthcare-focused ML and GenAI models (NLP, LLMs, predictive analytics) using PyTorch and TensorFlow for claims automation, medical coding, and patient risk scoring, achieving >95% accuracy on core NLP tasks. Built LLM-powered workflows using LangChain and agent-based pipelines for clinical document summarization and chart abstraction, reducing manual review effort by 35-40% while maintaining HIPAA compliance. Engineered large-scale data ingestion and feature pipelines on EHR and claims datasets using Spark, SQL, and Pandas, improving data quality checks and model readiness across high-volume healthcare workflows. Deployed end-to-end training, validation, and deployment pipelines with Docker, Kubernetes, and CI/CD (Azure DevOps), enabling seamless promotion from POC to production. Monitored real-time performance with Prometheus and Grafana, meeting SLAs of <100 ms latency and 99.9% uptime. Led POCs for ambiguous healthcare AI initiatives and mentored junior

AI/ML Engineer at Mphasis

January 1, 2021 - July 31, 2023

Planned and deployed Generative AI solutions using LLMs with LangChain, building RAG pipelines for intelligent document search, summarization, and Q&A workflows. Engineered end-to-end RAG architectures integrating vector databases and embeddings with SQL/NoSQL sources, improving retrieval accuracy by 30%+. Implemented ML/LLM pipelines with Python, PyTorch, TensorFlow, and Scikit-learn, applying prompt engineering and hyperparameter tuning to reduce hallucinations. Operationalized workflows with MLflow, Docker, Kubernetes, and CI/CD, enabling automated model versioning and repeatable deployments with 80%+ pipeline automation. Deployed GenAI services on AWS and Azure (Terraform IaC) with scalable inference endpoints, achieving <150 ms latency, >92% task accuracy, 99.5% uptime. Implemented drift and prompt-performance checks reducing post-deployment issues by 25-30%. Collaborated with U.S. product owners and DevOps in Agile sprints.

ML Engineer at Hexaware Technologies

September 1, 2019 - December 31, 2020

Created, trained, and evaluated ML models for enterprise use cases, including predictive analytics and anomaly detection, improving accuracy by 15-20% through feature engineering and hyperparameter tuning. Performed data preprocessing and validation on large datasets, reducing data quality issues by 30%. Built end-to-end ML pipelines with Docker and CI/CD for scalable deployment. Deployed models on cloud platforms (AWS/Azure) with latency under 200 ms. Implemented model versioning and monitoring with MLflow; conducted A/B testing and retraining triggers to maintain >90% performance. Collaborated with U.S. clients and cross-functional teams in Agile sprints.