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Hi, I'm Rhythm Bhavsar, a passionate Machine Learning Engineer with hands-on experience in leveraging advanced AI techniques such as Knowledge Graphs, Large Language Models, and NLP to build impactful solutions. I've had the opportunity to work on smart living systems, healthcare chatbots, and enterprise automation projects, blending my love for data science and programming to create intelligent, real-world applications. I thrive in collaborative environments where I get to blend technical expertise with creative problem solving. Whether it's fine-tuning LLaMA models or optimizing cloud deployments, I enjoy tackling challenges and continuously learning in this rapidly evolving field.…Hi, I'm Rhythm Bhavsar, a passionate Machine Learning Engineer with hands-on experience in leveraging advanced AI techniques such as Knowledge Graphs, Large Language Models, and NLP to build impactful solutions. I've had the opportunity to work on smart living systems, healthcare chatbots, and enterprise automation projects, blending my love for data science and programming to create intelligent, real-world applications. I thrive in collaborative environments where I get to blend technical expertise with creative problem solving. Whether it's fine-tuning LLaMA models or optimizing cloud deployments, I enjoy tackling challenges and continuously learning in this rapidly evolving field.

Rhythm Bhavsar

Data Scientist, AI Engineer, Developer, +1

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Hi, I'm Rhythm Bhavsar, a passionate Machine Learning Engineer with hands-on experience in leveraging advanced AI techniques such as Knowledge Graphs, Large Language Models, and NLP to build impactful solutions. I've had the opportunity to work on smart living systems, healthcare chatbots, and enterprise automation projects, blending my love for data science and programming to create intelligent, real-world applications. I thrive in collaborative environments where I get to blend technical expertise with creative problem solving. Whether it's fine-tuning LLaMA models or optimizing cloud deployments, I enjoy tackling challenges and continuously learning in this rapidly evolving field.…Hi, I'm Rhythm Bhavsar, a passionate Machine Learning Engineer with hands-on experience in leveraging advanced AI techniques such as Knowledge Graphs, Large Language Models, and NLP to build impactful solutions. I've had the opportunity to work on smart living systems, healthcare chatbots, and enterprise automation projects, blending my love for data science and programming to create intelligent, real-world applications. I thrive in collaborative environments where I get to blend technical expertise with creative problem solving. Whether it's fine-tuning LLaMA models or optimizing cloud deployments, I enjoy tackling challenges and continuously learning in this rapidly evolving field.

Available to hire

Hi, I’m Rhythm Bhavsar, a passionate Machine Learning Engineer with hands-on experience in leveraging advanced AI techniques such as Knowledge Graphs, Large Language Models, and NLP to build impactful solutions. I’ve had the opportunity to work on smart living systems, healthcare chatbots, and enterprise automation projects, blending my love for data science and programming to create intelligent, real-world applications.

I thrive in collaborative environments where I get to blend technical expertise with creative problem solving. Whether it’s fine-tuning LLaMA models or optimizing cloud deployments, I enjoy tackling challenges and continuously learning in this rapidly evolving field.

Skills

Experience Level

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

Machine Learning Engineer (Internship) at Dwellci AI

July 1, 2024 - Present

Worked on AI-powered smart living solutions by leveraging Knowledge Graphs, OWL ontologies, and Neo4j to structure and reason over contextual data. Designed and implemented graph-based data pipelines enabling dynamic, intelligent decision-making for residential environments. Fine-tuned LLaMA models with QLoRA on proprietary datasets to personalize responses and optimize inference latency. Integrated models into real-time applications with efficient serving and lightweight deployment. Managed full-cycle machine learning workflows including data preprocessing, feature engineering, model training, evaluation, and deployment on scalable infrastructure. Collaborated with cross-functional teams embedding models into production-grade microservices, ensuring robustness and maintainability in real-world environments.

Machine Learning (Experiential Learning Project) at CareEscapes AI

June 30, 2025 - July 31, 2025

Collaborated with a cross-functional team to develop a production-ready MVP chatbot using Retrieval-Augmented Generation (RAG) to streamline clinic appointment booking, reducing administrative friction and improving patient interaction efficiency. Designed a retrieval pipeline connecting domain-specific knowledge with QLoRA-fine-tuned LLaMA models, ensuring accurate and context-aware responses. Integrated semantic search with vector stores and custom embeddings for optimal retrieval. Deployed the solution on Azure Cloud, configuring scalable compute environments and storage, optimizing containerized deployments using Azure Container Instances and Function Apps. Strengthened experience in cloud-native ML, real-time NLP applications, and healthcare-oriented conversational AI systems.

Data Scientist at Voidek Webolutions

June 30, 2024 - July 31, 2025

Developed and deployed scalable Machine Learning models using AWS SageMaker, Lambda, and API Gateway, improving real-time data synchronization across CRM and ERP systems by 30% and reducing inconsistencies. Fine-tuned LLaMA model on personalized datasets to build an AI-powered chatbot, enhancing customer interactions and response accuracy. Experimented with LLMs like GPT and BERT to identify effective models for automating business-specific tasks and user engagement. Automated CRM-ERP integration using Deep Learning and NLP models, reducing manual intervention by 70% and improving data accuracy and workflow efficiency. Fetched and processed large-scale real-time data from Putty Server using SQL, deployed models via Flask-based APIs, and ensured seamless integration with enterprise applications for end-to-end automation.