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Chris Mu

@chris8

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GenAI Engineer 8+ years experience building secure, scalable solutions. Expert in LLMs, RAG, & MLOps

United States

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What I'm looking for

I'm looking for Principal Generative AI Engineer role where I can build and scale secure, production-grade solutions my expertise in LLMs, RAG, and multi-agent systems. My ideal position involves end-to-end system ownership, from architecture to deployment, in a collaborative environment that values innovation & strategic impact.

I am a Principal Generative AI Engineer with 8+ years of experience. I specialize in building scalable, secure AI solutions, focusing on LLMs, RAG, and agentic systems. My passion is to turn complex challenges into production-ready, impactful AI products.

Experience

Work history, roles, and key accomplishments

Current

Lead GenAI Engineer

Current

Progenix

Aug 2022 - Present (3 years)

I've built and deployed end-to-end ML models. I architected multi-agent RAG workflows, designed an LLM-powered financial document parser, and created automated data pipelines for vector DBs (Weaviate, Pinecone). I also implemented guardrails, observability (OpenTelemetry, LangSmith), and deployed cloud-native inference endpoints using Docker, ECS Fargate, and Terraform.

Senior Machine Learning Engineer

visibee

Feb 2020 - Jul 2022 (2 years 5 months)

Delivered production GenAI solutions for healthcare, legal, and finance clients. Built autonomous multi-agent systems for customer support, fraud detection, and contract analysis. I enhanced semantic search with knowledge graphs and fine-tuned custom LLMs using LoRA and RLHF. I also designed and implemented custom ML models in Python, Scikit-learn, and TensorFlow.

Machine Learning Engineer

Elementrix

Feb 2017 - Jan 2020 (2 years 11 months)

Developed an AI underwriting assistant using gradient boosting models and GPT-3 embeddings. Integrated LLM outputs into transaction monitoring and anomaly detection. I ensured compliance with KYC/AML regulations by safely applying AI in sensitive contexts, and built robust Python data pipelines for feature engineering and training ML models on large, unstructured datasets.