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Daniel Kan

@danielkan

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AI Systems Engineer at Xerix building LLM applications, RAG pipelines, and scalable model-serving infrastructure.

United States

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At Xerix, I develop LLM-powered applications using Generative AI, RAG, and prompt engineering. I also architect end-to-end AI/ML pipelines from data preprocessing through deployment and monitoring.

At AQSec, I built multi-agent workflows and RAG pipelines with LLMs, embeddings, and vector databases. I worked on agent systems that break complex business goals into sequential reasoning and planning steps.

At Clinix360, I developed and maintained AI/ML platforms for model training, deployment, and inference using Python and PyTorch. I deployed AI services with FastAPI, Docker, Kubernetes, AWS, and CI/CD pipelines.

Experience

Work history, roles, and key accomplishments

Current

Senior AI Systems Engineer

Current

Xerix

Mar 2023 - Present (3 years 7 months)

Developed LLM-powered applications using Generative AI, RAG, and prompt engineering. Architected end-to-end AI/ML pipelines and designed scalable model-serving infrastructure with Docker, Kubernetes, and cloud platforms.

Python PyTorch TensorFlow fastAPI Large Language Models LangGraph Model Optimization AI Architecture Agentic AI MLFlow Generative AI MLOps AWS Sagemaker Docker Kubernetes RAG

Agentic AI Engineer

AQSec

Nov 2020 - Feb 2023 (2 years 3 months)

Built multi-agent workflows and orchestration pipelines using LangGraph, CrewAI, AutoGen, and LangChain. Implemented RAG pipelines with vector databases and deployed agentic AI applications using Docker and cloud services.

LangGraph Crew AI AutoGen Lang Chain RAG Vector Databases Pinecone Milvus Docker Cloud Services Python

AI Platform Engineer

Clinix360

Jun 2018 - Oct 2020 (2 years 4 months)

Deployed AI services using FastAPI, Docker, and Kubernetes to improve scalability and reliability. Developed and maintained scalable AI/ML platforms for model training, deployment, and inference using Python and PyTorch.