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I am an AI Engineer with a strong background in developing and deploying AI-driven solutions, particularly in the areas of natural language processing and generative AI. My experience includes building AI tools for event management using LLMs and VLMs, improving search and recommendation systems with semantic search and embeddings, and creating sophisticated AI agents for lead generation, customer support, and ticket booking. I have also automated revenue extraction from documents using RAG pipelines and Gemini, and deployed event recommendation chatbots with optimized latency and cost-efficiency. My work has involved migrating ML models to AWS Cloud, building graph databases with Neo4j, and developing web crawlers. I have a proven ability to integrate diverse systems, automate workflows, and develop predictive models. My technical skills encompass a wide range of programming languages, ML/LLM frameworks, cloud platforms, and MLOps practices. I hold an M.S. in Big Data Analytics from San Diego State University and a B.Tech in Computer Science from BML Munjal University.…I am an AI Engineer with a strong background in developing and deploying AI-driven solutions, particularly in the areas of natural language processing and generative AI. My experience includes building AI tools for event management using LLMs and VLMs, improving search and recommendation systems with semantic search and embeddings, and creating sophisticated AI agents for lead generation, customer support, and ticket booking. I have also automated revenue extraction from documents using RAG pipelines and Gemini, and deployed event recommendation chatbots with optimized latency and cost-efficiency. My work has involved migrating ML models to AWS Cloud, building graph databases with Neo4j, and developing web crawlers. I have a proven ability to integrate diverse systems, automate workflows, and develop predictive models. My technical skills encompass a wide range of programming languages, ML/LLM frameworks, cloud platforms, and MLOps practices. I hold an M.S. in Big Data Analytics from San Diego State University and a B.Tech in Computer Science from BML Munjal University.

Sai Prasanth Paladugula

AI Engineer, Data Scientist, Full Stack Developer, +2

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I am an AI Engineer with a strong background in developing and deploying AI-driven solutions, particularly in the areas of natural language processing and generative AI. My experience includes building AI tools for event management using LLMs and VLMs, improving search and recommendation systems with semantic search and embeddings, and creating sophisticated AI agents for lead generation, customer support, and ticket booking. I have also automated revenue extraction from documents using RAG pipelines and Gemini, and deployed event recommendation chatbots with optimized latency and cost-efficiency. My work has involved migrating ML models to AWS Cloud, building graph databases with Neo4j, and developing web crawlers. I have a proven ability to integrate diverse systems, automate workflows, and develop predictive models. My technical skills encompass a wide range of programming languages, ML/LLM frameworks, cloud platforms, and MLOps practices. I hold an M.S. in Big Data Analytics from San Diego State University and a B.Tech in Computer Science from BML Munjal University.…I am an AI Engineer with a strong background in developing and deploying AI-driven solutions, particularly in the areas of natural language processing and generative AI. My experience includes building AI tools for event management using LLMs and VLMs, improving search and recommendation systems with semantic search and embeddings, and creating sophisticated AI agents for lead generation, customer support, and ticket booking. I have also automated revenue extraction from documents using RAG pipelines and Gemini, and deployed event recommendation chatbots with optimized latency and cost-efficiency. My work has involved migrating ML models to AWS Cloud, building graph databases with Neo4j, and developing web crawlers. I have a proven ability to integrate diverse systems, automate workflows, and develop predictive models. My technical skills encompass a wide range of programming languages, ML/LLM frameworks, cloud platforms, and MLOps practices. I hold an M.S. in Big Data Analytics from San Diego State University and a B.Tech in Computer Science from BML Munjal University.

Available to hire

I am an AI Engineer with a strong background in developing and deploying AI-driven solutions, particularly in the areas of natural language processing and generative AI. My experience includes building AI tools for event management using LLMs and VLMs, improving search and recommendation systems with semantic search and embeddings, and creating sophisticated AI agents for lead generation, customer support, and ticket booking. I have also automated revenue extraction from documents using RAG pipelines and Gemini, and deployed event recommendation chatbots with optimized latency and cost-efficiency. My work has involved migrating ML models to AWS Cloud, building graph databases with Neo4j, and developing web crawlers. I have a proven ability to integrate diverse systems, automate workflows, and develop predictive models. My technical skills encompass a wide range of programming languages, ML/LLM frameworks, cloud platforms, and MLOps practices. I hold an M.S. in Big Data Analytics from San Diego State University and a B.Tech in Computer Science from BML Munjal University.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at Events.com

July 1, 2024 - Present

Built AI-powered event tools including ad generation, event planning, descriptions, agendas, icebreakers, and event image generation using LLM APIs (Anthropic Claude, OpenAI GPT, Gemini), VLMs and text-to-image diffusion models (DALL-E, Gemini Flash, FLUX, Stable Diffusion). Improved event discovery search and recommendations via hybrid search using Pinecone vector embeddings, language models for query understanding, and metadata generation to raise Hit @ 10 from 0.74 to 0.83, and nDCG @ 20 by 3% in A/B testing.

Data Science Intern at Data Science Alliance, San Diego

March 1, 2024 - May 1, 2024

Developed a predictive model classifying high-priority customers using ensemble methods (XGBoost, Random Forest, LightGBM), and logistic regression with SMOTE; achieved 84% recall; deployed on GCP Vertex AI and visualized in Tableau.

AI Engineer Intern at Gravity AI, San Diego

October 1, 2023 - February 29, 2024

Contained and deployed 7 machine learning and deep learning models on AWS (ECS, SageMaker, Lambda, RDS, Redshift, SQS, CloudWatch, IAM), delivering stable, low-latency production inference pipelines. Automated deployment workflows (Terraform, Step Functions, CloudFormation) to ensure consistent model governance.

Data Science Analyst at Moores Cancer Center, San Diego

September 1, 2022 - October 1, 2023

Enhanced care for 1,000+ cancer patients by processing 100GB+ of EHR data using SVMs and transformer-based NLP models, reducing clinician review time from 5 minutes to 1 minute and lowering readmissions. Trained EfficientNetB3 models on 40k+ CT scans to classify multiple cancer types, achieving 94% accuracy and deploying via Azure Kubernetes Service for real-time clinical triage, leveraging deep learning. Created secure NLP pipelines for sentiment classification on 35k+ patient feedback entries, achieving 92% F1 and contributing to a 15% increase in NPS.

Data Science Engineer at Smart Energy Water

June 1, 2020 - July 1, 2022

Developed end-to-end energy-forecasting data pipelines for 700+ accounts using Prophet and K-means on Azure Databricks; automated data flows with Azure Data Factory/PySpark; performed A/B testing to confirm 5.5% reduction in energy bills. Designed anomaly detection with Isolation Forest and Autoencoders to identify early deviations, improving drift detection by 11%. Developed supervised ML models (Pandas, NumPy, Scikit-Learn, XGBoost) to predict customer behavior (89% accuracy). Built BER T-powered chatbot with NER and BI dashboard to monitor complaints and halve resolution time; automated OCR/NLP extraction from scanned meter readings and maintenance reports with Tesseract, OpenCV, Azure Computer Vision and TensorFlow achieving 95% data accuracy and 20% reduction in manual entry.