Architect, Data AI

Jaggaer•Durham, NC

About The Position

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 www.jaggaer.com 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'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.

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.

Nice To Haves

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.

Responsibilities

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 (SageMaker, 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.