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I am Parthavanaidu Jyotha, a Data Scientist and Generative AI/ML Engineer with hands-on experience designing, developing, and deploying scalable machine learning models, LLM applications, and AI solutions across cloud environments. I have a strong background in supervised and unsupervised learning, NLP, computer vision, deep learning, and MLOps. I am proficient in leveraging Gen AI frameworks, transformer models, and vector databases to build RAG systems, chatbots, and LLM fine-tuning pipelines. I have a proven track record of improving model performance by over 30%, reducing processing time by 40%, and increasing data pipeline efficiency by 45%.…I am Parthavanaidu Jyotha, a Data Scientist and Generative AI/ML Engineer with hands-on experience designing, developing, and deploying scalable machine learning models, LLM applications, and AI solutions across cloud environments. I have a strong background in supervised and unsupervised learning, NLP, computer vision, deep learning, and MLOps. I am proficient in leveraging Gen AI frameworks, transformer models, and vector databases to build RAG systems, chatbots, and LLM fine-tuning pipelines. I have a proven track record of improving model performance by over 30%, reducing processing time by 40%, and increasing data pipeline efficiency by 45%.

Parthavanaidu Jyotha

Data Scientist, AI Engineer, AI Developer, +2

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I am Parthavanaidu Jyotha, a Data Scientist and Generative AI/ML Engineer with hands-on experience designing, developing, and deploying scalable machine learning models, LLM applications, and AI solutions across cloud environments. I have a strong background in supervised and unsupervised learning, NLP, computer vision, deep learning, and MLOps. I am proficient in leveraging Gen AI frameworks, transformer models, and vector databases to build RAG systems, chatbots, and LLM fine-tuning pipelines. I have a proven track record of improving model performance by over 30%, reducing processing time by 40%, and increasing data pipeline efficiency by 45%.…I am Parthavanaidu Jyotha, a Data Scientist and Generative AI/ML Engineer with hands-on experience designing, developing, and deploying scalable machine learning models, LLM applications, and AI solutions across cloud environments. I have a strong background in supervised and unsupervised learning, NLP, computer vision, deep learning, and MLOps. I am proficient in leveraging Gen AI frameworks, transformer models, and vector databases to build RAG systems, chatbots, and LLM fine-tuning pipelines. I have a proven track record of improving model performance by over 30%, reducing processing time by 40%, and increasing data pipeline efficiency by 45%.

Available to hire

I am Parthavanaidu Jyotha, a Data Scientist and Generative AI/ML Engineer with hands-on experience designing, developing, and deploying scalable machine learning models, LLM applications, and AI solutions across cloud environments.

I have a strong background in supervised and unsupervised learning, NLP, computer vision, deep learning, and MLOps. I am proficient in leveraging Gen AI frameworks, transformer models, and vector databases to build RAG systems, chatbots, and LLM fine-tuning pipelines. I have a proven track record of improving model performance by over 30%, reducing processing time by 40%, and increasing data pipeline efficiency by 45%.

Skills

Experience Level

Expert

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Expert

Work Experience

Data Scientist at OpenAI

September 1, 2024 - Present

Developed Retrieval-Augmented Generation (RAG) systems using OpenAI, LangChain, and FAISS, improving response accuracy by 37%. Built LLM-based internal chatbot using GPT-4, Pinecone, and LangChain, reducing customer service query time by 42%. Fine-tuned open-source models (LLaMA2, Falcon) using domain-specific datasets and frameworks like Hugging Face and NLTK, increasing model accuracy by 28%. Deployed scalable AI models using Docker, Kubernetes, and FastAPI, reducing inference latency by 35%. Automated model training, evaluation, and tracking using MLflow and Airflow pipelines, boosting workflow efficiency by 40%. Engineered prompts for LLMs such as GPT-4, Claude, and Gemini to optimize QA and summarization tasks, improving output quality by 25%. Applied advanced NLP techniques using NLTK, Transformers, and LangChain for tasks like entity recognition, summarization, and classification. Performed A/B testing and model performance evaluation using SQL and Power BI to assess feature enh

Data Scientist at Amazon

November 1, 2023 - October 23, 2025

Developed supervised ML models for demand forecasting and personalized recommendations using Scikit-Learn, LightGBM, and XGBoost, improving accuracy by 32%. Built NLP pipelines for text classification, named entity recognition, and sentiment analysis using BERT, spaCy, and NLTK, increasing F1-score by 27%. Created REST APIs with Flask and FastAPI to serve ML models in real time, reducing inference latency by 38%. Implemented computer vision models with TensorFlow and OpenCV for defect detection, achieving 89% precision on production data. Optimized data pipelines using Apache Spark and Airflow, increasing data throughput by 45% and pipeline uptime by 33%. Used AWS SageMaker for end-to-end ML workflows, reducing model deployment time by 35% and cloud costs by 30%. Built interactive dashboards with Tableau, Seaborn, and Plotly to visualize model performance and business insights.

Data Scientist at OpenAI

September 1, 2024 - November 24, 2025

Implemented Retrieval-Augmented Generation (RAG) systems using OpenAI, LangChain, and FAISS to improve response accuracy by 37%. Built LLM-based internal chatbot with GPT-4, Pinecone, and LangChain, reducing customer service query time by 42%. Fine-tuned open-source models (LLaMA2, Falcon) on domain-specific data; deployed scalable AI models with Docker, Kubernetes, and FastAPI; automated model training/evaluation with MLflow/Airflow; engineered prompts for QA and summarization; performed NLP tasks including entity recognition, summarization, and classification; and conducted A/B testing to measure feature adoption.

Data Scientist at Amazon

November 1, 2023 - November 1, 2023

Developed supervised ML models for demand forecasting and personalized recommendations using Scikit-learn, LightGBM, and XGBoost, improving accuracy by 32%. Built NLP pipelines for text classification, NER, and sentiment analysis using BERT, spaCy, and NLTK; created REST APIs with Flask and FastAPI to serve models in real time; implemented computer vision models with TensorFlow and OpenCV for defect detection, achieving 89% precision on production data. Optimized data pipelines using Apache Spark and Airflow, increasing data throughput by 45% and pipeline uptime by 33%. Used AWS SageMaker for end-to-end ML workflows, reducing deployment time by 35% and cloud costs by 30%. Built interactive dashboards with Tableau, Seaborn, and Plotly to visualize model performance and business insights.