REFRAME Platform Architect

Position Information

• Working Title: REFRAME Platform Architect
• Posting Number: PG194519EP
• Position Number:
• Position Type: EPS/SAAO
• Full Time Equivalent (FTE): 1.0
• Appointment: 12 Month Recurring
• Recruitment Dates: Open Date: 03/05/2026;
  Anticipated Close Date: 03/05/2026
• Anticipated Hiring Range: Commensurate with Experience
• Work Schedule: Monday – Friday, 8am – 5pm (40 hours/week)
• Location: Department of Biological and Agricultural Engineering, NC State University

The Platform Architect will be an employee in the Department of Biological and Agricultural Engineering (BAE) jointly administered by the College of Agriculture and Life Sciences (CALS) and the College of Engineering at NC State University. The department actively contributes to the academic, research and extension missions of the University.

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This position supports the REFRAME project, a multi‑year, externally funded research initiative led by NC State University. The project aims to deliver a modular, AI‑enabled, open‑source platform for feedstock‑agnostic evaluation of future biomass. It will integrate legacy open‑source models, novel surrogates, shared metadata schemas, and a large language model (LLM) to enhance accessibility for varied stakeholders.

Leveraging well‑characterized agricultural and food processing residues, REFRAME will generate insights into historically underfunded circular biomass streams and provide deployment‑ready tools for end‑to‑end scenario analysis, counterfactual modeling, and decision support across the value chain. The Platform Architect will play a central role in designing and operationalizing the technical backbone of this platform in close collaboration with faculty, research staff, and external partners.

• Strategic Road mapping:
  Define the technical vision and multi‑year roadmap for the end‑to‑end ML platform, aligning it with project and research objectives and data science needs (including GenAI/LLM capabilities).
• System Design:
  Architect, design, and document a robust, scalable, and cost‑efficient platform covering the entire ML lifecycle:
  Data Ingestion, Feature Store, Model Training, Model Registry, Model Serving/Inference, and Monitoring.
• Technology Selection:
  Evaluate, select, and integrate appropriate cloud‑native services (AWS, Azure, or GCP), hybrid and on‑prem computing resources, and open‑source MLOps tools (e.g., Kubeflow, MLflow, Airflow) to build a cohesive ecosystem.

• Automation:
  Lead the implementation of MLOps pipelines using CI/CD practices to automate model training, testing, validation, deployment, and automated retraining workflows.
• Infrastructure as Code (IaC):
  Design and enforce IaC standards (Terraform/Cloud Formation) for provisioning and managing all underlying compute, networking, and storage resources (e.g., Kubernetes clusters, GPU instances).
• Feature Engineering:
  Define shared data and feature management patterns to ensure consistency and reuse across model training and inference workflows.

• Model Governance:
  Implement standards for model versioning, lineage tracking, and compliance to ensure models are traceable, reproducible, and meet ethical AI principles.
• Security:
  Architect security measures, including network segmentation, access control (IAM/RBAC), and data encryption across the entire ML pipeline.
• Performance & Cost Optimization:
  Establish comprehensive monitoring and…