Architect, Data AI

Architect, Data AI

JAGGAER provides an intelligent Source-to-Pay and Supplier Collaboration Platform that empowers organizations to manage and automate complex processes while enabling a highly resilient, responsible, and integrated supplier base. With 30 years of expertise, we specialize in solving complex procurement and supply chain challenges across various industries.

Our 1,300+ global employees are obsessed with ensuring customers get full value from our products - ultimately enhancing and transforming their businesses. For more information, visit

We are hiring an Architect, Data AI to lead the next generation of AI/ML across JAGGAER's Source-to-Pay and Supplier Collaboration platform. You'll set the technical direction across conventional ML, Generative AI, LLMs, Agentic AI, and RAG — and ship those capabilities into products used by 1,300+ enterprise customers and the global supply chains they run.

This is a hands-on technical leadership role. 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.

What Success Looks Like in 12 Months: A production agentic workflow live in the JAGGAER platform, automating a meaningful step of a customer's source-to-pay process. At least one Generative AI / RAG capability shipped to customers, with measurable adoption and a clear quality bar (groundedness, latency, cost per call). A documented AI/ML strategy and roadmap for the function — prioritized against business outcomes, with buy-in from product and engineering leadership.

• Set and own the AI/ML technical strategy for the platform — from model architecture to evaluation, deployment, and monitoring — and rally engineering and product leadership around it.
• Design, develop, and deploy machine learning models for prediction, classification, clustering, and time-series analysis.
• Develop Generative AI and LLM-powered solutions, including RAG pipelines for knowledge retrieval and contextual responses.
• Build and optimize Agentic AI systems capable of multi-step reasoning, tool orchestration, and autonomous workflows.
• Architect and manage vector database solutions (e.g., Pinecone, Weaviate, FAISS, Milvus) for embeddings, hybrid search, and RAG pipelines.
• Leverage advanced statistical and data science techniques to extract actionable insights from structured and unstructured datasets.
• Implement and scale AI/ML pipelines using AWS services (Sage Maker, Lambda, API Gateway, Bedrock, S3, EKS).
• Set the technical bar for the data science / ML function — design reviews, code and model reviews, technical standards, and upskilling peers and engineers around AI/ML best practices.
• Partner with business, product, and engineering leaders to translate procurement and supply chain problems into measurable AI/ML solutions.
• Write efficient, modular, and maintainable Python code for modeling, data processing, and deployment.
• Use advanced SQL for querying, transforming, and analyzing large relational datasets.
• Establish standards for model evaluation, observability, and responsible AI — including documentation, reproducibility, and guardrails for LLM and agent systems.

• 14–15 years of experience in data science / applied ML, including 3–4 years building production Generative AI and Agentic AI systems with Lang Chain, Lang Graph, and Lang Flow.
• 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 Lang Chain, Lang Graph, Lang Flow, or similar agent frameworks.
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