Corbion-posted 1 day ago
Full-time • Mid Level
Onsite • Lenexa, KS
1,001-5,000 employees

At Corbion, we exist to champion preservation in all its forms, preserving food and food production, health, and our planet. Corbion’s Data and AI Lab is building the future of intelligent systems—where AI agents collaborate, reason, and act autonomously across our global operations. We’re looking for a Machine Learning / AI Engineer to help us bring this vision to life, working at our Lenexa, KS office. In this role, you’ll design and deploy ML models and agentic AI systems that power decision-making across R&D, manufacturing, supply chain, and commercial domains. You’ll work at the intersection of data science, software engineering, and domain expertise to create scalable, cloud-native solutions that drive measurable business impact.

  • Build intelligent systems: Develop ML models and agent-based AI systems that reason, learn, and act across domains like procurement, production, and customer engagement.
  • Engineer autonomy: Design multi-agent architectures where AI agents collaborate to optimize operations from 'Idea to Launch', 'Demand to Supply', ‘Order to Cash’, etc.
  • Operationalize AI: Lead MLOps practices for model lifecycle management, including monitoring, retraining, and governance.
  • Collaborate cross-functionally: Partner with data scientists, analytics engineers, and business stakeholders to translate complex problems into AI-powered solutions.
  • Accelerate innovation: Contribute to the development of AI-enhanced digital products, predictive tools, and decision support systems.
  • Bachelor’s Degree or Master’s Degree in Computer Science, Mathematics, Data Science, AI, or related field.
  • 3+ years of experience in ML engineering, applied AI, or intelligent systems development.
  • Strong Python skills and experience with ML frameworks (e.g., MLlib, scikit-learn, TensorFlow, PyTorch).
  • Experience with cloud platforms (preferably Azure) and CI/CD pipelines.
  • Solid understanding of data modeling, software architecture, and algorithmic design.
  • Agentic AI frameworks (e.g., LangChain, AutoGen, CrewAI) or multi-agent systems.
  • LLM orchestration, retrieval-augmented generation (RAG), or domain-specific copilots.
  • Azure AI Foundry, Apache Spark and data lake platforms.
  • Real-time data pipelines, event-driven architectures, or streaming analytics.
  • Food tech, biotech, or manufacturing environments.
  • AI ethics, explainability, and responsible AI practices.
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