Remote AI/ML Engineer—LLMs, RAG & Cloud

YO AI Labs; California (MO); Remote; USD 120,000 - 180,000; Full time

Job summary

YO AI Labs is seeking an experienced AI/ML Engineer to build and deploy secure, scalable AI solutions for mission-critical initiatives. You will work with LLMs, RAG, prompt engineering, multi-agent systems, and cloud AI platforms to develop production-grade ML applications.

You will design, implement, and optimize AI/ML solutions, deploy across secure cloud environments, and collaborate with product, security, and engineering teams to ensure robust, scalable systems.

Qualifications

• Proficient in Python for AI/ML development.
• Experience with LLMs, RAG, and prompt engineering.
• Hands-on with multi-agent orchestration and tool use.
• Knowledge of cloud AI services (GovClouds, Vertex AI, Bedrock).
• Experience building data pipelines, ETL, and ontologies.
• Strong secure coding and CI/CD practices.

Responsibilities

• Design, implement, and optimize AI/ML solutions using LLMs, RAG, and prompt engineering.
• Develop and orchestrate multi-agent systems using LangGraph and LangChain.
• Deploy AI solutions across secure cloud environments (GovClouds, Vertex AI, Bedrock).
• Build robust ETL/data pipelines, metadata catalogs, and ontologies for training and inference.
• Develop and maintain REST APIs and SDK integrations.
• Collaborate with product, security, and engineering teams to deliver secure, scalable solutions.
• Follow secure coding, DevOps, and CI/CD practices.

Tools

LangGraph

LangChain

AWS GovCloud

Google GovCloud

Azure IL5+

Vertex AI

AWS Bedrock

Job description

YO AI Labs is seeking an experienced AI/ML Engineer to build and deploy secure, scalable AI solutions for mission-critical initiatives. You will work with LLMs, RAG, prompt engineering, multi-agent systems, and cloud AI platforms to develop production-grade ML applications.

You will design, implement, and optimize AI/ML solutions, deploy across secure cloud environments, and collaborate with product, security, and engineering teams to ensure robust, scalable systems.