Senior AI Engineer, Procurement Sciences AI

• Strong foundation in traditional machine learning with production deployment experience
• Excited to apply ML fundamentals to modern AI workflows including RAG and basic agent systems
• Collaborative problem solver who thrives in startup environments with evolving requirements
• Eager to learn new technologies while leveraging proven ML engineering practices
• 5+ years deploying ML models in production (classification, recommendations, or similar)
• Strong Python proficiency with ML libraries (scikit-learn, pandas, numpy) and deployment frameworks
• Experience with ML infrastructure: model serving, monitoring, and data pipelines
• Familiarity with foundation model APIs (OpenAI, Anthropic, etc.) and vector databases
• Track record of building systems that handle real user traffic and data
• U.S. citizenship with the ability to pass a Federal Background Check and Identity Verification
• While formal education is not a strict requirement, a Bachelor's or Master’s degree in Computer Science, Engineering, or a related field is preferred

Desirable

• We are seeking a Machine Learning Engineer to build and scale classification and recommendation systems that power our core product
• You'll work closely with ML leadership to apply proven ML fundamentals across diverse technical challenges, including integrating LLM-based features into our existing workflows
• In this role, you will own traditional ML systems while gaining hands-on experience with modern AI components like RAG pipelines and agent frameworks
• Your work will directly impact product performance through better recommendations, search quality, and user classification
• You'll collaborate with a talented team to solve real technical problems in a fast-growing SaaS environment
• Build, deploy, and maintain production classification and recommendation systems serving thousands of users
• Design and implement ML pipelines for training, evaluation, and monitoring of traditional ML models
• Integrate LLM APIs and vector databases into existing ML workflows to enhance product capabilities
• Collaborate with product and engineering teams to translate business requirements into scalable ML solutions
• Optimize model performance, system reliability, and inference latency across our ML stack