Jinal S.

I am an AI Engineer with 5+ years of experience in developing AI solutions, GenAI applications, and end-to-end data pipelines. I specialize in developing AI solutions using OpenAI, Gemini, Llama, Retrieval-Augmented Generation (RAG), AI Agents, Computer Vision, and FastAPI. I have experience delivering AI solutions across document intelligence, recruitment platforms, enterprise management systems, workflow automation, and business process optimization. My focus is on building scalable, secure, and high-performance AI applications that solve real business problems. ➔ Core Capabilities • Generative AI & LLM Systems: Custom RAG pipelines with FAISS and vector search, GraphRAG, reflective agentic workflows, local LLM orchestration with Ollama, Hugging Face, and LLaMA, and prompt engineering and fine-tuning. • Machine Learning & Deep Learning: Time-series forecasting using LSTM, RNN, and Prophet, deep learning with PyTorch and TensorFlow, and custom neural architectures. • Computer Vision & Geospatial Data: Real-time 3D landmark tracking using OpenCV and OpenSeeFace, satellite remote sens • End-to-End MLOps & Cloud Engineering: Scalable API deployment using FastAPI and Flask, Docker containerization, AWS infrastructure including EC2 and S3, GCP including Vertex AI, Cloud Run, and BigQuery, and continuous integration pipelines. ➔ Key Project Highlights • Legal RAG Compliance Platform: Built a two-pass RAG retrieval engine using FAISS and vector search, exposed via a GPU-accelerated FastAPI service for real-time compliance analysis. • AI Document Intelligence Platform Designed and developed an AI-powered document processing solution to automate cargo bill extraction. • AI-Powered Project Management System Developed a comprehensive enterprise project management platform with an AI-powered assistant capable of managing employees, projects, clients, agreements, and company policies through natural language interactions. • AI Recruitment & Talent Intelligence Platform Developed intelligent APIs for recruitment automation, including company intelligence, job title matching, semantic skill matching, salary normalization, commute analysis, and candidate recommendation using Gemini models. ➔ Tech Stack Summary • Languages: Python, SQL, Bash • AI/ML Frameworks: PyTorch, TensorFlow, LangChain, LangGraph, Hugging Face, Scikit-learn, OpenCV • Databases & Vector Search: FAISS, MySQL, PostgreSQL, SQLite, BigQuery • Deployment & Cloud: Docker, AWS, GCP, Streamlit, FastAPI, REST APIs. Whether you need to take an AI project from proof-of-concept to production, build custom RAG pipelines, or deploy robust machine learning models, let us connect and discuss your goals!