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🧠 AI, LLM & Agent-Based Solutions I’m an AI Engineer and Python Developer specializing in Artificial Intelligence, Machine Learning, and LLM Agent Development. I deliver Generative AI, ChatGPT-based Chatbots, and Automation Systems that transform data into intelligent business solutions. With expertise in Deep Learning, NLP, and Model Fine-Tuning, I build custom AI models, voice AI assistants, and scalable AI APIs powered by OpenAI, LangChain, and Hugging Face. ✅ Fine-tuning LLM models for Classification, Named Entity Recognition (NER), Summarization, Q&A, and Sentiment Analysis ✅ Building Recommendation Systems and Forecasting/Prediction Models ✅ n8n and RelevanceAI :API Integration, AI Automation, and Workflow Development ⚙️ Technologies I Work With 🐍 Python & AI Ecosystem Python | FastAPI |Pandas | NumPy |Seaborn |Matplotlib |Regex Machine Learning: Scikit-learn, XGBoost, traditional ML models (classification, regression, clustering) NLP: SpaCy, NLTK, word2vec, TF-IDF, LSTM, RNN, GRU Computer Vision: Transformers, OpenCV, Convolutional Neural Networks(CNN), YOLO API Integration: REST API | OpenAI API | ChatGPT API | Gemini API | Huggingface Models API AI Frameworks: PyTorch, TensorFlow, Keras, LangChain Databases & Vector Store: Pinecone, ChromaDB for Retrieval-Augmented ,Firebase(cloud database) , SQL (PostgreSQL, MySQL), NoSQL (MongoDB) 💻 Full Stack & AI Integration Deployment: Docker, AWS, Azure Frontend: Streamlit, HTML/CSS Backend: Flask API, Fast API, Node.js, Next.js 🛠️ AI & Automation Services 🔍 Web Scraping & Data Automation | Data Collection, Cleaning & Parsing 📊 Data Science & Machine Learning Models | Predictive Analytics | Feature Engineering 🤖 ChatGPT & AI Agent Development | Conversational AI 🗣️ Voice AI Integration | Text-to-Speech | Speech-to-Text | Audio Processing ⚡ AI Model Development | Prompt Engineering | Generative AI Implementation 📌 Whether you need an AI chatbot, fine-tuned LLM, creative AI system (image, video, voice), I’ll help bring your vision to life with robust, production-ready AI. 🔗 Let’s connect and discuss how we can transform your ideas into powerful AI-driven solutions

Hi! I’m Ratnapriya, a Senior Data Scientist specializing in Machine Learning, NLP and predictive modeling. I build end-to-end solutions that turn audio, text and tabular data into reliable, production-ready systems; from data design and feature engineering to model training, evaluation and deployment. What I deliver: • Text summarization, sentiment, topic & NLP pipelines (transformers + classical) • Speech emotion & audio analysis (MFCCs, spectrograms) • Predictive models & lead scoring (XGBoost, tabular pipelines) • Clustering/EDA for root-cause analysis and automation workflows I focus on clear results, reproducible code, and outputs non-technical teams can use. I can start immediately.

I have 6+ years experience as an ML/NLP Engineer. I worked with Micro1 where we developed a smart networking app which was aimed at encouraging and reconnecting old friends to meet where an AI bot came up with plans matching friends and planning the outing based on preferences. I managed the entire ML framework for this app combining Gen AI tools with ML algorithms for matching. I have experience with finetuning gen AI tools, RAG, using copilot to enhance code. I worked with a company to develop a virtual assistant using gpt 3.5 and pincone with the help of langchain using RAG I have worked on various data extraction and classification models. I work on Python and have worked with various libraries, Spacy, scikit learn, pandas, numpy being a few. I have been working remotely for the last 5 years so I am familiar with working on projects on remote servers and version control through git. I have a Masters Degree in Computer Science from the University of Southern California.

I am an AI/ML Engineer specializing in Generative AI, Large Language Models (LLMs), AI Agents, Retrieval-Augmented Generation (RAG), and intelligent automation. My work focuses on designing and deploying production-ready AI solutions that combine state-of-the-art language models with scalable backend systems to solve real-world business problems. My expertise spans the complete AI development lifecycle, from data collection and preprocessing to model training, fine-tuning, evaluation, deployment, and monitoring. I have hands-on experience building AI-powered applications using OpenAI, Anthropic, Google Gemini, Llama, Qwen, Mistral, DeepSeek, Gemma, Phi, and other modern foundation models. I build custom AI assistants, enterprise chatbots, multi-agent systems, and RAG pipelines using frameworks such as LangChain, LangGraph, LlamaIndex, CrewAI, and AutoGen. I work extensively with vector databases including Pinecone, ChromaDB, FAISS, Weaviate, Qdrant, and Milvus, implementing semantic search, hybrid search, reranking, and knowledge retrieval systems. My NLP expertise includes text classification, sentiment analysis, named entity recognition, summarization, question answering, topic modeling, information extraction, semantic similarity, document intelligence, and multilingual language understanding. I also have experience developing multimodal AI applications that combine text, images, and structured data. I am experienced in fine-tuning open-source LLMs using LoRA, QLoRA, PEFT, Hugging Face Transformers, TRL, and distributed training techniques. My work includes instruction tuning, domain adaptation, synthetic data generation, evaluation pipelines, and optimization for inference efficiency. Beyond LLMs, I have strong experience in classical machine learning, deep learning, computer vision, recommendation systems, time series forecasting, and predictive analytics using frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, and CatBoost. I also design scalable AI infrastructure and APIs using FastAPI, Docker, Kubernetes, Redis, PostgreSQL, MongoDB, Celery, WebSockets, and cloud platforms including AWS, Azure, and Google Cloud. My focus is on building reliable, secure, and production-ready AI systems that integrate seamlessly with existing business workflows. My recent work includes: * AI Agents capable of autonomous reasoning and tool usage. * Enterprise RAG systems with advanced retrieval, reranking, and citation support. * LLM-powered document processing and knowledge management platforms. * Recommendation engines and personalized AI applications. * Real-time AI systems with streaming inference and WebSocket communication. * Fine-tuning and deployment of open-source LLMs for domain-specific use cases. * End-to-end MLOps pipelines for model training, deployment, monitoring, and continuous improvement. I am also proficient in data engineering tasks including web scraping, data annotation, ETL pipelines, feature engineering, and working with large-scale structured and unstructured datasets. My research interests include LLM reasoning, multimodal AI, AI Agents, Retrieval-Augmented Generation, reinforcement learning, efficient model fine-tuning, and trustworthy AI. I continuously explore the latest advancements in Generative AI to deliver practical, scalable, and impactful AI solutions. Whether you need an intelligent AI assistant, an enterprise chatbot, a production-ready RAG system, an autonomous AI agent, custom model fine-tuning, or an end-to-end AI application, I can help transform your ideas into robust, high-performing solutions.

I build AI systems that reason, not just match vectors. I’m an Senior Ontologist + Graph AI Engineer with 4+ years of production experience building RAG, GraphRAG, AI agents, and semantic knowledge systems with Neo4j, OWL ontologies, and LLMs. I’ve worked with Fractal Analytics, Mercedes-Benz, Philips, and QpiAI on intelligent RAG and knowledge systems. Core expertise: - GraphRAG, RAG & semantic search - Knowledge Graphs, Neo4j & Cypher - Ontology Engineering — OWL, SWRL, SKOS, RDF - Hybrid retrieval, re-ranking & query expansion - LLMs, OpenAI, LLaMA & Gemini - Pydantic & structured LLM outputs - RDF/JSON-LD data transformation - MLOps & production deployment If you’re building AI that needs to understand relationships and reason over knowledge, I can help.

I am an AI Engineer with 4+ years of experience building and deploying production-ready AI systems across classical machine learning, deep learning, computer vision, NLP, and Generative AI. Unlike many AI developers who focus only on LLMs, I work across the entire AI stack. I believe the best solution isn't always a large language model or an expensive API. Many real-world problems are better solved using classical machine learning or deep learning, resulting in lower infrastructure costs, faster inference, reduced latency, and greater control over your solution. My goal is always to build the most effective system not the most expensive one. Some of the areas I regularly work in include: * Classical Machine Learning (XGBoost, LightGBM, CatBoost, Random Forests, SVMs, feature engineering, predictive modelling, forecasting, anomaly detection, recommendation systems) * Deep Learning (PyTorch, TensorFlow, CNNs, Transformers, Vision Transformers, knowledge distillation, model optimization) * Computer Vision (object detection, image classification, segmentation, OCR, document understanding, face recognition, multi-object tracking, embedding-based search) * NLP & LLMs (RAG, GraphRAG, agentic workflows, fine-tuning, embeddings, semantic search, document QA, information extraction) * Generative AI applications using OpenAI, Anthropic, Gemini, and open-source models * End-to-end AI pipelines from data collection and preprocessing to training, evaluation, deployment, and monitoring I also have extensive experience optimizing AI models for production through knowledge distillation, pruning, quantization, and efficient inference, making models smaller, faster, and more cost-effective for both cloud and edge deployments. On the engineering side, I work comfortably with Python, FastAPI, PostgreSQL, pgvector, asynchronous programming, Docker, GPU acceleration, and cloud deployments. I build complete AI products and APIs that are designed to scale not just research prototypes. Beyond implementation, I enjoy solving difficult research and engineering problems. Whether it's designing a predictive model, improving model accuracy, reducing inference costs, building an intelligent document processing pipeline, or deploying an LLM application, I focus on solutions that are reliable, maintainable, and practical for production. I also lead a team of AI engineers, giving me experience not only in technical execution but also in planning, code quality, mentoring, and delivering projects on time. If you're looking for someone who can understand the problem first, choose the right AI approach, and build a production-ready solution that balances performance, cost, and scalability, I'd be happy to help.