ragav541036 User

I’m a results-driven AI engineer focused on moving Large Language Models (LLMs) from concept to production. I specialize in architecting multi-agent orchestration platforms and enterprise-grade RAG systems that improve both accuracy and real-world usability.

At Holoware (2021–Present), I architect and deploy production-ready generative AI solutions and agentic workflows using LangChain and CrewAI. I’ve built multi-agent systems that automate complex business research, reducing manual data processing time by ~35%, and I optimize RAG pipelines for high-accuracy document retrieval across massive enterprise datasets. I also lead technical roadmap discussions for AI adoption across hardware and software product lines.

My recent work includes an autonomous AI software engineer that performs high-level task decomposition, web research, and independent code execution within sandboxed Docker environments. I developed a multi-agent orchestration layer using LangGraph to coordinate specialized sub-agents for planning, coding, and automated testing, and I implemented a constrained Agent-Computer Interface (ACI) so the agent can interact with a terminal and file system—achieving a [XX]% success rate on the SWE-bench (real-world GitHub issue resolution). I’ve also integrated dynamic state tracking and a conversational UI for real-time human-in-the-loop (HITL) feedback and created a DAG-based workflow engine for 24/7 autonomous data scraping and report generation.

I bring practical expertise across fine-tuning methods like LoRA/QLoRA, strong engineering with APIs and infrastructure, and an outcome-focused mindset that reduces operational costs while enhancing decision-making speed.