PRINCIPAL AI ARCHITECT @ VIRIDIAN TECH INC
As a Principal AI Architect, you will take the lead in designing comprehensive end-to-end AI systems, including RAG and multi-agent architectures. This role requires a hands-on approach to building and deploying production-grade AI/LLM applications, developing scalable APIs and AI services using Python and FastAPI. You will be tasked with optimizing LLM performance regarding latency, cost, and hallucination reduction, ensuring that the organization stays at the forefront of AI innovation by evaluating and adopting the latest tools, frameworks, and models.
Beyond technical execution, the Principal AI Architect provides critical technical leadership and architecture reviews across the full AI lifecycle. You will collaborate closely with cross-functional teams to deliver high-quality solutions while establishing engineering standards and reusable components. The role also encompasses a governance mandate, ensuring that all AI systems are monitored for compliance and performance standards. Candidates must be comfortable working in a remote capacity within the United States and adhering to the PST time zone.
Beyond technical execution, the Principal AI Architect provides critical technical leadership and architecture reviews across the full AI lifecycle. You will collaborate closely with cross-functional teams to deliver high-quality solutions while establishing engineering standards and reusable components. The role also encompasses a governance mandate, ensuring that all AI systems are monitored for compliance and performance standards. Candidates must be comfortable working in a remote capacity within the United States and adhering to the PST time zone.
Key Requirements
Minimum of 12 years of professional experience in software or AI architecture
Deep expertise in Large Language Models (LLMs) and RAG systems
Proficiency with Agentic AI frameworks such as LangChain and LangGraph
Strong background in Python, SQL, and FastAPI for API development
Extensive experience with Vector Databases like FAISS and Pinecone
Knowledge of hybrid retrieval methods and Knowledge Graphs
Hands-on experience with major cloud platforms like AWS, GCP, or Azure
Experience implementing Docker and CI/CD pipelines for AI applications
Solid foundations in Machine Learning, NLP, and deep learning
Ability to provide technical leadership and conduct high-level architecture reviews