AI Engineer

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Full/Part-time:
Full time
Job Category:
Analytics
City:
Chicago

HAVI is a global, privately owned company focused on innovating, optimizing and managing the supply chains of leading brands. Offering services in marketing analytics, packaging, supply chain management and logistics, HAVI partners with companies to address challenges big and small across the supply chain, from commodity to customer. Founded in 1974, HAVI employs more than 10,000 people and serves customers in more than 100 countries.

HAVI’s supply chain services are complemented by the customer engagement services offered by our affiliated company The Marketing Store. For more information, please visit

Design, build, and deploy scalable product‑ready artificial intelligence solutions to support our predictive and prescriptive analytics needs. Act as a trusted partner and advisor to solutions architects and data scientists and become a crucial part of the analytics solution lifecycle – from prototype to production and operations of our data science and advanced analytics solutions in areas such as promotions, supply and demand planning, item/menu level analytics, supply chain simulations, and optimization, competitive benchmarking, and root cause analysis.

Continuously improve and advance our AI solutions, aligned with evolving industry best practices.

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.

• 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)
• 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 Tensor Flow, PyTorch, Scikit-learn
• Leverage cloud platforms (AWS, Azure, GCP) and MLOps tools (MLflow, Kubeflow)
• Implement Agentic AI capability to drive efficiency and opportunity

• 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 Dev Ops:
  Git branching, code reviews, unit/integration testing (pytest/dbx), CI/CD (Azure Dev Ops/Git Hub Actions)
• Infrastructure as Code (Terraform or Bicep) for Databricks work spaces, 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

• Content scope: AI Engineering, Data engineering, Agentic AI and automation, and ML/AI solution operationalization
• Geographical Scope:
  Global
• Stakeholders & Networks:
  Analytics and Insights, Dev Ops, Product Management, Technology
• Working model:
  Individual contributor,…