AI/ML Systems Engineer — Build Production Agents

Supermicro; Wayne (CA); On-site; USD 90,000 - 120,000; Full time

Job summary

Supermicro is seeking an experienced on-site ML/AI developer to build AI agents that process customer support and technical tickets. You will collaborate with software developers and cross-functional teams to improve support efficiency and reduce turnaround times.

Responsibilities include creating end-to-end AI pipelines, connecting data sources, presenting updates to stakeholders, and defining success metrics for production-ready services built around large language models.

Qualifications

• Bachelor's degree in Computer Science, Mathematics, IT, or related field (or equivalent practical experience).
• Software engineering background with production services experience.
• Strong proficiency in Python and ML/LLM frameworks.
• Proficiency with Linux, Git, and Docker.
• Experience with SQL, NoSQL, and vector databases.
• Experience building production-ready systems that use large language models.
• Strong problem-solving and communication skills.
• Experience handling confidential customer data.

Responsibilities

• Create and maintain end-to-end AI agent pipelines that handle high-volume support and technical tickets.
• Connect and process data from internal and external sources.
• Present demonstrations and updates to internal stakeholders.
• Define success metrics and iterate deployed services based on production feedback.

Skills

Python

ML frameworks

Communication skills

Problem-solving

Data confidentiality

Education

Bachelor's degree in CS/Math/IT or related field

Tools

Docker

Linux

Git

SQL

NoSQL

Vector databases

LangChain

Job description

Supermicro is seeking an experienced on-site ML/AI developer to build AI agents that process customer support and technical tickets. You will collaborate with software developers and cross-functional teams to improve support efficiency and reduce turnaround times.

Responsibilities include creating end-to-end AI pipelines, connecting data sources, presenting updates to stakeholders, and defining success metrics for production-ready services built around large language models.