Mid AI/LLM Engineer (Remote)

We’re looking for an AI Engineer / LLM Engineer who can turn AI concepts into reliable, production-ready applications using Snowflake and large language models (LLMs).

This is a hands-on engineering role focused on building, deploying, monitoring, and maintaining production AI systems—not AI research.

🔹 KEY RESPONSIBILITIES

• Build, deploy, and maintain production-ready AI applications on Snowflake, including LLM-powered applications, Retrieval-Augmented Generation (RAG), and AI agents.
• Translate AI designs and technical direction into scalable, reliable, and monitored production services.
• Develop and optimize data preparation workflows, SQL queries, and integrations within Snowflake.
• Implement LLM solutions, including prompt management, evaluation, and performance optimization.
• Establish and maintain CI/CD pipelines, automated testing, monitoring, and incident troubleshooting for live AI systems.
• Collaborate with data scientists and engineering teams to ensure AI solutions meet business requirements and production standards.
• Monitor application performance, troubleshoot issues, and continuously improve the reliability and efficiency of deployed AI systems.

🔹 QUALIFICATIONS

• 3–5 years of experience in AI, Machine Learning, Data Engineering, or Software Engineering.
• Strong proficiency in Python and SQL.
• Hands-on experience with Snowflake, including data preparation, performance tuning, and integrations.
• Proven experience deploying and maintaining production LLM applications, particularly RAG systems and AI agents.
• Experience with prompt management, LLM evaluation, testing, CI/CD, monitoring, and troubleshooting live systems.
• Strong problem-solving skills and the ability to translate technical requirements into production-ready solutions.

🔹 NICE TO HAVE

• Experience with Snowflake Cortex, including Cortex Agents, Search, or Analyst, and/or Snowpark.
• Familiarity with LLM evaluation and observability tools.
• Experience with traditional ML pipelines, including model deployment, drift monitoring, and retraining.
• Exposure to the Microsoft AI ecosystem, such as Azure AI, Power Platform, or Microsoft Copilot.

-bn/lgc