AI Engineer

About the role

This is a hybrid role based at 345 N Morgan St, Chicago, IL 60607. Candidates must reside in the Chicago metropolitan area. Relocation assistance is not offered at this time.

Responsibilities

• Responsible for working with the data management, data science, decision science, and technology teams to address supply chain data needs in demand and supply planning, replenishment, pricing, and optimization
• Design and train machine learning (ML) and deep learning (DL) models
• Select appropriate algorithms based on the problem (e.g., classification, regression, NLP, computer vision)
• Collect, clean, and preprocess large datasets
• Work with structured and unstructured data (text, images, audio, etc.)
• Deploy models into production environments (e.g., using APIs, cloud platforms)
• Maintain and monitor model performance and retrain as needed
• Stay updated with the latest AI research and tools
• Experiment with new architectures (e.g., transformers, GANs)
• Translate business problems into AI solutions
• Use frameworks like TensorFlow, PyTorch, Scikit-learn
• Leverage cloud platforms (AWS, Azure, GCP) and MLOps tools (MLflow, Kubeflow)
• Implement Agentic AI capability to drive efficiency and opportunity

Qualifications

• Bachelor’s degree in computer science, data science, information systems, information science or a related field; advanced degree in computer science, data science, information systems, information science or a related field preferred
• 2+ years hands-on Azure Databricks (PySpark/Scala, Spark SQL, Delta Lake)
• Experience with Azure Data Factory for orchestration (pipelines, triggers, parameterization, IRs) and integration with ADLS Gen2, Key Vault
• Strong SQL expertise across large datasets; performance tuning (joins, partitions, file sizing)
• Data quality at scale (e.g., Great Expectations/Deequ), monitoring and alerting; debug/backfill playbooks
• Experience with DevOps: Git branching, code reviews, unit/integration testing (pytest/dbx), CI/CD (Azure DevOps/GitHub Actions)
• Infrastructure as Code (Terraform or Bicep) for Databricks workspaces, cluster policies, ADF, storage
• Observability & cost control: Azure Monitor/Log Analytics; cluster sizing, autoscaling, Photon; cost/perf trade-offs
• Proven experience collaborating with cross-functional stakeholders (analytics, data governance, product, security) to ship and support data products
• Certifications such as Microsoft Azure AI Engineer Associate, AWS Certified Machine Learning professional, or other AI/ML/cloud/MLOps certifications a plus