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I’m Eswari Sirigineedi, an AI/ML Engineer with 4+ years of experience delivering scalable ML and Generative AI solutions across healthcare and finance. I specialize in MLOps pipelines, real-time data engineering, and low-latency production deployments using AWS SageMaker, Kubeflow, Airflow, Spark, and MLflow. I’m passionate about turning data into intelligent, production-ready AI systems that deliver measurable business impact. I also develop RAG-based LLM applications with LangChain and Pinecone, implement robust governance and performance monitoring, and enjoy collaborating with cross-functional teams to build data-driven decision platforms that optimize outcomes for users and stakeholders.…I’m Eswari Sirigineedi, an AI/ML Engineer with 4+ years of experience delivering scalable ML and Generative AI solutions across healthcare and finance. I specialize in MLOps pipelines, real-time data engineering, and low-latency production deployments using AWS SageMaker, Kubeflow, Airflow, Spark, and MLflow. I’m passionate about turning data into intelligent, production-ready AI systems that deliver measurable business impact. I also develop RAG-based LLM applications with LangChain and Pinecone, implement robust governance and performance monitoring, and enjoy collaborating with cross-functional teams to build data-driven decision platforms that optimize outcomes for users and stakeholders.

Eswari Sirigineedi

AI Engineer, AI Developer, Data Scientist, +2

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I’m Eswari Sirigineedi, an AI/ML Engineer with 4+ years of experience delivering scalable ML and Generative AI solutions across healthcare and finance. I specialize in MLOps pipelines, real-time data engineering, and low-latency production deployments using AWS SageMaker, Kubeflow, Airflow, Spark, and MLflow. I’m passionate about turning data into intelligent, production-ready AI systems that deliver measurable business impact. I also develop RAG-based LLM applications with LangChain and Pinecone, implement robust governance and performance monitoring, and enjoy collaborating with cross-functional teams to build data-driven decision platforms that optimize outcomes for users and stakeholders.…I’m Eswari Sirigineedi, an AI/ML Engineer with 4+ years of experience delivering scalable ML and Generative AI solutions across healthcare and finance. I specialize in MLOps pipelines, real-time data engineering, and low-latency production deployments using AWS SageMaker, Kubeflow, Airflow, Spark, and MLflow. I’m passionate about turning data into intelligent, production-ready AI systems that deliver measurable business impact. I also develop RAG-based LLM applications with LangChain and Pinecone, implement robust governance and performance monitoring, and enjoy collaborating with cross-functional teams to build data-driven decision platforms that optimize outcomes for users and stakeholders.

Available to hire

I’m Eswari Sirigineedi, an AI/ML Engineer with 4+ years of experience delivering scalable ML and Generative AI solutions across healthcare and finance. I specialize in MLOps pipelines, real-time data engineering, and low-latency production deployments using AWS SageMaker, Kubeflow, Airflow, Spark, and MLflow. I’m passionate about turning data into intelligent, production-ready AI systems that deliver measurable business impact.

I also develop RAG-based LLM applications with LangChain and Pinecone, implement robust governance and performance monitoring, and enjoy collaborating with cross-functional teams to build data-driven decision platforms that optimize outcomes for users and stakeholders.

Skills

Experience Level

Expert

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Expert

Language

English

Fluent

Work Experience

AI/ML Engineer at Humana

May 1, 2025 - Present

Architected and deployed real-time data pipelines using Apache Kafka and Spark Streaming, enabling seamless processing of millions of daily healthcare transactions to power AI-driven patient care applications. Engineered ETL workflows with Apache Airflow and dbt, automating complex data transformations and loading processes into Snowflake for enterprise-grade analytics dashboards. Implemented Retrieval-Augmented Generation (RAG) pipelines using LangChain, FAISS, and Hugging Face LLMs, enabling context-aware GenAI assistants for internal provider documentation and claims analysis. Developed and deployed a PySpark + MLflow risk-analytics platform that reduced fraud-detection latency by 30% and ensured HIPAA compliance for 10M+ healthcare records. Deployed scalable machine learning models on AWS SageMaker with Kubernetes orchestration, ensuring low-latency predictions for high-traffic healthcare applications. Established data governance framework with DataHub and Great Expectations, enfor

Gen AI Intern at AIG

November 1, 2024 - April 1, 2025

Developed generative AI tools using large language models and LangChain, automating insurance claims processing and documentation workflows to reduce manual effort. Designed and A/B-tested GPT-prompt frameworks, boosting chatbot response accuracy by 35% and shortening inquiry-resolution time by 20%. Built retrieval-augmented generation (RAG) pipelines with Pinecone and Azure, enabling efficient knowledge retrieval for automated policy summarization tasks. Collaborated with cross-functional teams to deploy secure LLM prototypes on Azure, ensuring compliance with strict insurance industry regulations and standards. Fine-tuned large language models to improve output safety and relevance, integrating evaluation metrics to maintain robustness in production environments.

Machine Learning Engineer at Persistent

June 1, 2020 - July 1, 2023

Created interactive analytics dashboards using Power BI and Tableau, enabling self-service reporting and data-driven decision-making for over 25 cross-functional teams. Engineered AWS Glue + PySpark ETL pipelines, automating ingestion of 1 TB+ datasets and reducing data-refresh cycles from hours to under 20 minutes, accelerating analytics delivery for 25+ teams. Deployed ML models on AWS SageMaker using Hugging Face libraries, enhancing predictive capabilities for business-critical applications. Designed fraud detection models with XGBoost, analyzing financial transaction data to identify and flag suspicious patterns with high precision. Implemented scalable data lakes on AWS S3 with Delta Lake, ensuring reliable and efficient storage for enterprise analytics and reporting systems. Automated MLOps pipelines using Kubeflow and GitHub Actions, streamlining model training, validation, and deployment for multiple models monthly. Optimized Amazon Redshift queries to accelerate dashboard loa