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Mid-level, metro ML role with generalist title and employer brand, attracting many qualified applicants.
Strong ML engineering skills are transferable, but knowledge-graph and domain-specific requirements increase industry specificity moderately.
Requires 5+ years, production ML experience and knowledge-graph expertise, enforcing strict technical filters.
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Own the end-to-end lifecycle of Machine Learning and AI systems focused on the Food and Beverage science domain.
Design datasets, evaluation criteria, and test AI system performance including cost, accuracy, and reliability.
Lead experimentation and code design across teams, collaborating with engineering, product, and sales to optimize AI deliverables and customer experience.
Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, Machine Learning, or related field, or equivalent experience.
Minimum of 5 years in software engineering, data engineering, AI/ML engineering or related roles with production-grade system delivery.
Experience in Python production services and data pipelines; knowledge of C# is a plus.
At least 2 years experience with Azure cloud preferred; AWS or GCP acceptable; strong understanding of knowledge graphs, semantic modeling, and AI evaluation metrics required.
Experience working independently and across teams with minimal supervision, including cross-functional collaboration with product and sales.
Proven ability to design and interpret complex AI/ML evaluation metrics impacting user experience and business metrics.
Technical background combining AI/ML engineering expertise with software engineering best practices, including agentic coding paradigms.