Architect, Data AI

Our 1,300+ global employees are obsessed with ensuring customers get full value from our products - ultimately enhancing and transforming their businesses. We are hiring a Architect, Data AI to lead the next generation of AI/ML across JAGGAER's Source-to-Pay and Supplier Collaboration platform. You will architect production-grade AI systems, raise the technical bar across data science and ML engineering, and partner directly with product, engineering, and customer-facing leaders to translate procurement and supply chain problems into measurable AI outcomes — spend intelligence, supplier risk, contract understanding, autonomous sourcing workflows, and beyond. Position Requirements • 14–15 years of experience in data science / applied ML, including 3–4 years building production Generative AI and Agentic AI systems with LangChain, LangGraph, and LangFlow.• Track record of technical leadership without direct reports — setting architecture, driving cross-team alignment, and shipping AI/ML into production at enterprise scale.• Proven expertise in conventional ML techniques: regression, classification, clustering, time-series forecasting, and predictive modeling.• Proven track record of developing and deploying Generative AI, LLM-based, RAG-based, and Agentic AI solutions.• Experience with LangChain, LangGraph, LangFlow, or similar agent frameworks.• Strong proficiency in Python for machine learning, data manipulation, and deployment.• Advanced SQL skills for working with large relational datasets, including hands-on experience with Snowflake (warehousing, performance tuning, and integration with ML/AI pipelines).• Hands-on experience with AWS services (SageMaker, Bedrock, Lambda, EKS, API Gateway, S3).• Hands-on experience with vector databases (e.g., Pinecone, Weaviate, FAISS, Milvus) as a core component of RAG pipelines.• Familiarity with data engineering principles and cloud-based data pipelines.• Strong judgment translating ambiguous business problems into concrete AI/ML solutions — and the discipline to know when ML is the wrong tool.• Preferred Qualifications (Good-to-Have)• Exposure to Model Context Protocol (MCP) for orchestrating AI applications.• Background in MLOps/CI-CD pipelines for deploying and monitoring ML models at scale.• Familiarity with deep learning frameworks (TensorFlow, PyTorch) for advanced modeling. What We Offer:  At JAGGAER, we are committed to supporting you and your family’s well-being.

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