I am a GenAI Engineer with 3 years of experience shipping production large language model systems across finance, healthcare, and sales.
I focus on retrieval augmented generation, agentic workflows, multi-agent orchestration, and building reliable AI systems that can move from prototype to production.
My work spans the full stack of GenAI delivery, from data preparation and embeddings to guardrails, evaluation, and client-facing implementation.
I have worked with Python, TypeScript, Node.js, LangChain, LangGraph, FastAPI, PostgreSQL, and pgvector to build practical AI products and internal automation.
I have also led and founded AI-driven delivery processes in a digital marketing agency, where I helped turn manual workflows into scalable systems.
I am open to remote opportunities and interested in roles where I can build robust LLM applications, AI agents, and production-grade automation systems.
Engineered an agentic AI over a proprietary knowledge base with adaptive RAG, financial embeddings, and real-time query optimization for autonomous decisions on live data. Built a code-based validation pipeline between reasoning and execution, added cascading risk guardrails, and implemented a self-improvement loop.
Automated a pre-market briefing that reduced daily research time from almost three hours to under five minutes. Orchestrated media, economic releases, market positioning, and synthesis with source-failure resilience, structured scoring, and confidence handling.
Integrated LLM-based automation into digital marketing delivery processes, making the agency more scalable and less dependent on manual work. Built practical AI workflows for content, copy, and campaign delivery.
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