AI/ML Engineer specialized in production-grade LLM systems, RAG pipelines, and agentic AI. Transforming research into reliable, scalable solutions.
Passionate about building production-ready AI systems that solve real problems at scale
I'm Gopinath Boyapalli, an AI/ML Engineer who thrives on transforming cutting-edge research into production-ready systems. My expertise lies in building scalable LLM applications, RAG systems, and multi-agent architectures that deliver measurable business value.
What drives me is the challenge of solving complex technical problems while maintaining system reliability, performance, and cost-effectiveness. I don't just build demos—I engineer solutions that handle real-world traffic, scale gracefully, and provide 99.9% uptime.
"My approach: Deep dive into the problem, architect for scale, implement with precision, and iterate based on real user feedback. Production-first, always."
Currently seeking opportunities with fast-scaling teams where AI is core to the product and engineering decisions directly impact business outcomes.
Production RAG & Fine-tuning
Multi-agent orchestration
AWS, Docker, Kubernetes
CI/CD, Monitoring, Scaling
Production-grade systems built with measurable impact and proven scalability
Production-grade multi-agent system handling 1000+ concurrent orders with distributed locks, MongoDB sharding, and DeepSeek LLM integration for intelligent order processing.
Enterprise RAG system with orchestrator coordinating Web Search, Retriever (10K+ docs), and Image Generation agents. Context-aware routing achieved 40% faster queries.
Optimized fine-tuning pipeline for DeepSeek-R1 using LoRA/QLoRA on 1M instructions. Achieved 1.31 validation loss with 16K context and 60% faster training.
Production NLP pipeline with BERT-base for sentence pair classification on GLUE MRPC. Achieved 89.97 F1 score, surpassing baseline by 5% with optimized training pipeline.
Enterprise-grade RAG combining FAISS vector search (70%) with BM25 keyword matching (30%). Indexed 7,712 ArXiv ML papers with comprehensive evaluation framework and Docker deployment.
Novel MCTS-based automated security testing for LLM applications. Detects 15+ attack vectors including prompt injection, jailbreaks, and data exfiltration with industry-leading 94.2% detection rate.
AI-powered multi-agent system for personal financial management using Model Context Protocol (MCP). 5 specialized agents coordinate for expense tracking, budgeting, investment advice, and financial reports.
Built a complete transformer-based language model from first principles. Implemented attention mechanisms, positional encoding, and training pipeline to understand LLM architecture at a fundamental level.
"Gopinath's ability to translate complex AI concepts into production-ready systems is exceptional. His RAG implementation reduced our query latency by 40% while maintaining high accuracy."