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I'm a data scientist with over a decade of experience designing, deploying, and monitoring scalable ML, NLP, and Generative AI solutions across Banking, Network, Healthcare, and Retail domains. I specialize in end-to-end ML lifecycle management, cloud-based deployments on AWS, Azure, and Google Cloud, and building production-ready AI microservices using FastAPI, Docker, and Kubernetes. I’ve led GenAI-powered query assistants and RAG-based customer engagement solutions that improve satisfaction while reducing operational load. I thrive in cross-functional, Agile teams, translating business needs into practical AI solutions. My focus includes vector databases, LangChain, and model monitoring to ensure performance and safety in production environments, with a strong emphasis on collaboration with data engineers, product managers, and software developers.…I'm a data scientist with over a decade of experience designing, deploying, and monitoring scalable ML, NLP, and Generative AI solutions across Banking, Network, Healthcare, and Retail domains. I specialize in end-to-end ML lifecycle management, cloud-based deployments on AWS, Azure, and Google Cloud, and building production-ready AI microservices using FastAPI, Docker, and Kubernetes. I’ve led GenAI-powered query assistants and RAG-based customer engagement solutions that improve satisfaction while reducing operational load. I thrive in cross-functional, Agile teams, translating business needs into practical AI solutions. My focus includes vector databases, LangChain, and model monitoring to ensure performance and safety in production environments, with a strong emphasis on collaboration with data engineers, product managers, and software developers.

Chandra Sekhar Gondrala

Data Scientist, AI Engineer, Data Analyst, +2

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I'm a data scientist with over a decade of experience designing, deploying, and monitoring scalable ML, NLP, and Generative AI solutions across Banking, Network, Healthcare, and Retail domains. I specialize in end-to-end ML lifecycle management, cloud-based deployments on AWS, Azure, and Google Cloud, and building production-ready AI microservices using FastAPI, Docker, and Kubernetes. I’ve led GenAI-powered query assistants and RAG-based customer engagement solutions that improve satisfaction while reducing operational load. I thrive in cross-functional, Agile teams, translating business needs into practical AI solutions. My focus includes vector databases, LangChain, and model monitoring to ensure performance and safety in production environments, with a strong emphasis on collaboration with data engineers, product managers, and software developers.…I'm a data scientist with over a decade of experience designing, deploying, and monitoring scalable ML, NLP, and Generative AI solutions across Banking, Network, Healthcare, and Retail domains. I specialize in end-to-end ML lifecycle management, cloud-based deployments on AWS, Azure, and Google Cloud, and building production-ready AI microservices using FastAPI, Docker, and Kubernetes. I’ve led GenAI-powered query assistants and RAG-based customer engagement solutions that improve satisfaction while reducing operational load. I thrive in cross-functional, Agile teams, translating business needs into practical AI solutions. My focus includes vector databases, LangChain, and model monitoring to ensure performance and safety in production environments, with a strong emphasis on collaboration with data engineers, product managers, and software developers.

Available to hire

I’m a data scientist with over a decade of experience designing, deploying, and monitoring scalable ML, NLP, and Generative AI solutions across Banking, Network, Healthcare, and Retail domains. I specialize in end-to-end ML lifecycle management, cloud-based deployments on AWS, Azure, and Google Cloud, and building production-ready AI microservices using FastAPI, Docker, and Kubernetes. I’ve led GenAI-powered query assistants and RAG-based customer engagement solutions that improve satisfaction while reducing operational load.

I thrive in cross-functional, Agile teams, translating business needs into practical AI solutions. My focus includes vector databases, LangChain, and model monitoring to ensure performance and safety in production environments, with a strong emphasis on collaboration with data engineers, product managers, and software developers.

Skills

Experience Level

Expert

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Language

English

Fluent

Work Experience

Data Scientist at Bank of America

July 1, 2024 - Present

Led the development of GenAI-powered applications to automate and enhance financial customer service operations, including optimizing support chatbots using RAG pipelines and LangChain agentic workflows. Built scalable LLM pipelines on AWS Bedrock and SageMaker, integrating OpenAI and Claude APIs with fine-tuned prompts and embedding-based filtering to reduce hallucinations. Developed secure, scalable Python APIs with OAuth2 and JWT for exposing ML-powered services to internal tools and customer-facing applications. Designed and deployed Retrieval-Augmented Generation (RAG) pipelines using Azure OpenAI, Azure AI/Search, and vector databases to enable contextual, retrieval-grounded responses in enterprise chat applications. Implemented agentic AI workflows for secure multi-agent orchestration, enabling tool calling and process automation. Deployed sentiment analysis, keyword extraction, and complaint classification to optimize call center performance. Orchestrated AI microservices with

AI Engineer at CVS Health

March 1, 2022 - June 1, 2024

Built GenAI capabilities leveraging Hugging Face Transformers and LLaMA for multi-label classification on architectural documentation. Integrated NLP pipelines with LangChain and vector search components for semantic search and clustering. Applied fine-tuning techniques like LoRA and PEFT to improve model accuracy; built GraphRAG for structured outputs. Developed predictive models (logistic regression, random forest, XGBoost, KNN, SVM) and deployed them as scalable APIs using Flask in Docker/Kubernetes; built real-time inference pipelines on AWS; implemented dashboards with AWS CloudWatch, QuickSight, and Tableau/Power BI. Led cross-functional collaboration in Agile environments; designed data warehouse models (Snowflake) and automated data refresh cycles. Built data pipelines with Spark/Scala and Python for large-scale ETL; deployed RAG pipelines for architectural docs; integrated with Vertex AI for fine-tuning. Implemented monitoring and guardrails; produced real-time analytics dashb

Gen-AI/AI Engineer at CareSource

August 1, 2019 - February 1, 2022

Built AI/ML pipelines for predictive maintenance and capacity planning on network data; developed Retrieval-Augmented Generation (RAG) pipelines with LangChain and vector databases (FAISS, Pinecone) for enterprise document summarization and semantic search. Fine-tuned BERT/T5 variants with LoRA/PEFT for multilingual document processing; deployed to Azure/AWS using Kubernetes orchestrated microservices. Implemented time-series modeling for predictive maintenance and SLA monitoring; created dashboards in Power BI to visualize model outputs and KPIs; built end-to-end data pipelines using Spark, SQL, and ETL. Integrated structured and unstructured data sources; collaborated in Agile teams; explored Vision-Language Models for video search. Implemented guardrails to ensure safe generation.

Data Analyst at State of New York

February 1, 2016 - July 1, 2019

Performed data manipulation and aggregation for healthcare analytics; built dashboards in QlikView and Power BI; developed predictive models for high-risk patients and fraud detection; used Spark DataFrames, Scala MLlib, and Erwin for data modeling; maintained data lineage and data quality across domains; supported Agile data product development and data governance.

Data Analyst at Morgan Stanley

February 1, 2014 - January 1, 2016

Built structured fraud intelligence reports using PL/SQL and Oracle; automated daily/weekly reporting with Unix shell scripts; contributed to re-architecting Informatica workflows with metric definitions and QA plans. Implemented data integrity checks, traced data lineage for discrepancy resolution, and developed dashboards for stakeholder dashboards; collaborated with analysts and ETL teams to align with fraud detection thresholds.