Shubham
Ai Engineer @Spineor Webservices Pvt. Ltd
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WORK HISTORY
Ai Engineer @Spineor Webservices Pvt. Ltd
Sahibzada Ajit Singh Nagar, IN
Designed and fine-tuned custom LLM models for enterprise automation, content understanding, and chat-based solutions. Built complete RAG pipelines using vector databases, embeddings, and document chunking for real-time knowledge retrieval. Created robust NLP conversational systems, document QA models, and intelligent search solutions. Developed scalable AI microservices using FastAPI + Docker for production-grade deployments. Worked closely with frontend/backend teams to integrate AI into React and Node.js applications. Delivered LLM-enabled automation tools that reduced support workload by 55%. Led solution architecture, client communication, and end-to-end AI delivery.
ABOUT SHUBHAM
As an AI Engineer with 5+ years of hands-on experience, I specialize in building AI systems powered by Large Language Models, RAG pipelines, deep learning, and scalable machine learning architectures. I transform complex business challenges into intelligent, automated solutions that drive measurable impact.I’ve led end-to-end development of AI products — from model design, fine-tuning, and evaluation to deploying microservices that seamlessly integrate with enterprise applications. My work spans LLMs, NLP, computer vision, time-series forecasting, optimization, and MLOps, enabling companies to adopt AI confidently and at scale. What I Do Design, fine-tune & optimize custom LLMs for chatbots, automation, and enterprise knowledge systems Build complete RAG pipelines using vector databases, embeddings, and real-time retrieval mechanisms Develop NLP solutions, document QA models, intelligent search systems & conversational agents Architect scalable ML microservices with FastAPI, Docker, and modern deployment workflows Implement computer vision models achieving production-level accuracy Deliver time-series forecasting solutions for financial & operational analytics Lead architecture decisions, technical strategy, and cross-team collaboration Impact Highlights Delivered LLM-driven automation reducing client support workload by 55% Achieved 93% accuracy in production computer vision systems Improved forecasting accuracy by 18% using advanced time-series models Reduced model latency by 30% through systematic optimization and profiling Built reusable, scalable ML pipelines adopted across multiple client projects Tech StackLLMs · RAG · Prompt Engineering · Vector Databases · PyTorch · NLP · Computer Vision · FastAPI · Docker · MLflow · Optuna · React/Node Integrations · MLOps
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