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Mid-level ML role, metro location, and popular title drive high candidate competition.
Core ML skills are transferable but domain-specific knowledge graphs and F&B focus increase sensitivity to domain fit.
Explicit 5+ years, production ML delivery, knowledge-graph expertise, and cloud experience raise shortlisting strictness.
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Own the end-to-end lifecycle of Machine Learning and AI systems in the Food and Beverage science domain.
Design datasets, evaluation criteria, and test cases; measure and monitor cost, performance, and accuracy of AI systems.
Lead experimentation and code design around ML systems, collaborate with engineering, product, and sales teams, and contribute to AI product deliverables.
Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, Machine Learning, or related technical field, or equivalent practical experience.
5+ years of professional experience in software engineering, data engineering, AI/ML engineering, or related, with demonstrated ability to ship production-grade systems.
Experience in building production services, data pipelines, and integrations using Python; additional C# experience is ideal.
2+ years experience with Azure cloud platform preferred; AWS or GCP alternatively; production-grade experience with knowledge graphs, graph databases, semantic modeling, or ontology concepts.
Proven ability to independently design and interpret AI/ML model evaluation metrics focusing on quality, relevance, latency, cost, and reliability.
Experienced in working cross-functionally with product, engineering, and sales teams without explicit leadership instructions.
Familiar with modern agentic coding paradigms and capable of educating others while accelerating software development.