I am an Agentic AI Engineer with over 5 years of hands-on experience in designing, orchestrating, and deploying multi-agent LLM systems in production environments. My expertise centers on building scalable, autonomous, and business-driven AI agents for SaaS and enterprise applications, and I have a strong background in Python, Kubernetes, Docker, and leading LLM frameworks like LangChain and RAG.
I specialize in AI agent orchestration, agent communication protocols, memory management, and business outcome optimization. My technical toolkit includes MLOps platforms such as MLflow and Airflow, cloud infrastructure (AWS, GCP, Azure), modern databases like Pinecone and Weaviate, and knowledge graphs such as Neo4j. I’m also deeply familiar with LLMs, including OpenAI GPT, Anthropic Claude, and Hugging Face Transformers.
In my recent roles, I’ve led the full-stack delivery of agentic AI systems, managed end-to-end automation pipelines, and contributed to both research and open-source projects. My work has resulted in peer-reviewed publications at conferences like NeurIPS and ICCV. I’m passionate about building robust, outcome-oriented AI systems, and I always look for ways to combine technical excellence with real business value. I also enjoy mentoring junior engineers and fostering a collaborative engineering culture.More...