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Hi, I’m Sathvik Vadlapatla, an AI/ML engineer specializing in Generative AI, RAG pipelines, and LangChain. I design scalable NLP and computer-vision solutions that turn complex data into actionable insights, with hands-on experience deploying on AWS, Azure, and GCP. I enjoy building practical AI-powered products, from chatbots and sentiment analysis to forecasting and fraud detection, and I’m passionate about delivering robust, maintainable ML systems through teamwork and clear communication. I’m motivated by solving real-world problems with explainable models and have a track record of enabling faster decision-making and improved customer outcomes through data-driven solutions.…Hi, I’m Sathvik Vadlapatla, an AI/ML engineer specializing in Generative AI, RAG pipelines, and LangChain. I design scalable NLP and computer-vision solutions that turn complex data into actionable insights, with hands-on experience deploying on AWS, Azure, and GCP. I enjoy building practical AI-powered products, from chatbots and sentiment analysis to forecasting and fraud detection, and I’m passionate about delivering robust, maintainable ML systems through teamwork and clear communication. I’m motivated by solving real-world problems with explainable models and have a track record of enabling faster decision-making and improved customer outcomes through data-driven solutions.

Sathvik Vadlapatla

Data Scientist, AI Engineer, AI Developer, +4

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Hi, I’m Sathvik Vadlapatla, an AI/ML engineer specializing in Generative AI, RAG pipelines, and LangChain. I design scalable NLP and computer-vision solutions that turn complex data into actionable insights, with hands-on experience deploying on AWS, Azure, and GCP. I enjoy building practical AI-powered products, from chatbots and sentiment analysis to forecasting and fraud detection, and I’m passionate about delivering robust, maintainable ML systems through teamwork and clear communication. I’m motivated by solving real-world problems with explainable models and have a track record of enabling faster decision-making and improved customer outcomes through data-driven solutions.…Hi, I’m Sathvik Vadlapatla, an AI/ML engineer specializing in Generative AI, RAG pipelines, and LangChain. I design scalable NLP and computer-vision solutions that turn complex data into actionable insights, with hands-on experience deploying on AWS, Azure, and GCP. I enjoy building practical AI-powered products, from chatbots and sentiment analysis to forecasting and fraud detection, and I’m passionate about delivering robust, maintainable ML systems through teamwork and clear communication. I’m motivated by solving real-world problems with explainable models and have a track record of enabling faster decision-making and improved customer outcomes through data-driven solutions.

Available to hire

Hi, I’m Sathvik Vadlapatla, an AI/ML engineer specializing in Generative AI, RAG pipelines, and LangChain. I design scalable NLP and computer-vision solutions that turn complex data into actionable insights, with hands-on experience deploying on AWS, Azure, and GCP. I enjoy building practical AI-powered products, from chatbots and sentiment analysis to forecasting and fraud detection, and I’m passionate about delivering robust, maintainable ML systems through teamwork and clear communication.

I’m motivated by solving real-world problems with explainable models and have a track record of enabling faster decision-making and improved customer outcomes through data-driven solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI/Machine Learning Engineer at Unum Group

August 1, 2024 - Present

Designed and deployed NLP pipelines integrating Named Entity Recognition (NER) with sentiment analysis, boosting document mining efficiency by 70% and enabling actionable insights from customer feedback and insurance claim notes. Built a Generative AI chatbot leveraging LLMs with LangChain and RAG pipelines, retrieving knowledge base content from Confluence, which improved customer service response quality and enhanced satisfaction scores by 25%. Developed a custom BERT-based model for aspect-based sentiment analysis, achieving an F1 score of 0.85, enabling granular extraction of customer insights that influenced key business strategy and product experience improvements. Engineered scalable LSTM-based models and unsupervised clustering on large policyholder and claims datasets, raising prediction accuracy by 20% and improving downstream analytics for customer engagement and fraud detection.

Machine Learning Engineer at Aspire Technolab

May 1, 2023 - October 8, 2025

Developed NLP models for sentiment analysis, topic modeling, and spam detection, achieving an F1-score of 0.92 and enhancing customer engagement by reducing unwanted communications across email and in-app channels. Enhanced sales forecasting accuracy by 15% using ARIMA and XGBoost models, generating insights into product demand cycles and seasonal revenue trends, visualized through interactive dashboards for retail business stakeholders. Automated and optimized ETL pipelines with PySpark and AWS Glue, processing 500GB+ of daily transaction data from thousands of retail outlets, reducing processing time by 40%, and ensuring accurate data availability for analytics. Streamlined ML deployment with AWS CodePipeline and SageMaker, cutting deployment time by 50% and enabling continuous integration/continuous delivery (CI/CD), ensuring faster iteration and monitoring of predictive retail models.

AI/Machine Learning Engineer at Unum Group

August 1, 2024 - Present

Machine Learning Engineer at Aspire Technolab

May 31, 2023 - October 8, 2025

Developed NLP models for sentiment analysis, topic modeling, and spam detection, achieving an F1-score of 0.92 and enhancing customer engagement by reducing unwanted communications across email and in-app channels. Enhanced sales forecasting accuracy by 15% using ARIMA and XGBoost models, generating insights into product demand cycles and seasonal revenue trends, visualized through interactive dashboards for retail business stakeholders. Automated and optimized ETL pipelines with PySpark and AWS Glue, processing 500GB+ of daily transaction data from thousands of retail outlets, reducing processing time by 40%, and ensuring accurate data availability for analytics. Streamlined ML deployment with AWS CodePipeline and SageMaker, cutting deployment time by 50% and enabling CI/CD, ensuring faster iteration and monitoring of predictive retail models.

AI/Machine Learning Engineer at Unum Group

August 1, 2024 - Present

Designed and deployed NLP pipelines integrating Named Entity Recognition (NER) with sentiment analysis to boost document mining efficiency by 70% and enable actionable insights from customer feedback and insurance claim notes. Built a Generative AI chatbot leveraging LLMs with LangChain and RAG pipelines, retrieving knowledge base content from Confluence, which improved customer service response quality and enhanced satisfaction scores by 25%. Developed a custom BERT-based model for aspect-based sentiment analysis achieving an F1 score of 0.85, enabling granular extraction of customer insights that influenced key business strategy and product experience improvements. Engineered scalable LSTM-based models and unsupervised clustering on large policyholder and claims datasets, raising prediction accuracy by 20% and improving downstream analytics for customer engagement and fraud detection.

Machine Learning Engineer at Aspire Technolab

May 1, 2023 - October 8, 2025

Developed NLP models for sentiment analysis, topic modeling, and spam detection, achieving an F1-score of 0.92 and enhancing customer engagement by reducing unwanted communications across email and in-app channels. Enhanced sales forecasting accuracy by 15% using ARIMA and XGBoost models, generating insights into product demand cycles and seasonal revenue trends, visualized through interactive dashboards for retail business stakeholders. Automated and optimized ETL pipelines with PySpark and AWS Glue, processing 500GB+ of daily transaction data from thousands of retail outlets, reducing processing time by 40% and ensuring accurate data availability for analytics. Streamlined ML deployment with AWS CodePipeline and SageMaker, cutting deployment time by 50% and enabling CI/CD, ensuring faster iteration and monitoring of predictive retail models.