AI Engineer

HAVI België; Chicago, Northern (IL, KY); Hybrid; USD 105,000 - 125,000; Full time

Benefits offered by this job

Hybrid work model

Bonus eligible

Job summary

HAVI is seeking a Design, build, and deploy scalable AI solutions expert to support predictive analytics across supply chain domains in a hybrid role based in Chicago.

You will work with data management, data science, and technology teams to deploy ML models, optimize AI pipelines, and translate business problems into AI solutions. Strong Azure Databricks and cloud skills are required.

Qualifications

• Bachelor’s degree in computer science, data science, information systems or related field; advanced degree preferred.
• 2+ years hands-on Azure Databricks (PySpark/Scala, Spark SQL, Delta Lake).
• Experience with Azure Data Factory orchestration and ADLS Gen2 integration.
• Strong SQL across large datasets; performance tuning.
• Data quality at scale and monitoring; DevOps experience.
• IaC for Databricks workspaces and pipelines (Terraform/Bicep).
• Observability, cost control, and cloud ML lifecycle tooling.
• Certifications in Azure AI / ML полезно.

Responsibilities

• Collaborate with data management, data science, and tech teams on supply chain analytics.
• Design and train ML/DL models; select algorithms for various tasks.
• Prepare data from structured and unstructured sources; preprocess large datasets.
• Deploy models to production via APIs and cloud platforms.
• Monitor and retrain models; stay updated with AI research.
• Use TensorFlow, PyTorch, Scikit-learn; leverage AWS/Azure/GCP.
• Implement agentic AI capabilities to improve efficiency.

Skills

Azure Databricks

SQL

DevOps

Python

PySpark

Spark SQL

ML/DL frameworks

TensorFlow

PyTorch

Azure

GCP

AWS

MLOps

GitHub Actions

Kubeflow

Terraform/Bicep

Azure Monitor

Education

Bachelor’s degree in computer science or related field

Advanced degree preferred

Tools

Azure Databricks

Azure Data Factory

ADLS Gen2

Key Vault

Terraform/Bicep

Azure Monitor / Log Analytics

MLflow / Kubeflow

GitHub Actions / Azure DevOps

Python / PySpark

Cloud platforms (AWS/Azure/GCP)

Job description

HAVIis 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. For more information, please visit HAVI.com .

Overiew:

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.

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).
• 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

Dimensions & Stakeholders:

• Content scope: AI Engineering, Data engineering, Agentic AI and automation, and ML/AI solution operationalization
• Geographical Scope: Global
• Stakeholders & Networks: Analytics and Insights, DevOps, Product Management, Technology
• Working model: Individual contributor, collaborates with cross-functional team within global planning and analytics

Salary Range:

• $105,000-$125,000 with a 5% targeted bonus

Total Rewards:

Our total rewards philosophy integrates programs for compensation, benefits, recognition, learning and development, corporate culture, corporate citizenship and work-life balance. While individual program components may differ by country, some things remain constant:

• Our commitment to rewarding results
• Opportunities to work with talented and driven individuals at every level of our company who respect each other, treat each other fairly and hold one another accountable for our customers’—and our company’s—success

• Inclusive employee resource groups
• Generous medical, dental, vision and other great benefits
• Paid parental and medical leave programs
• 401(k) with a company match component and profit sharing
• 15 days of paid time off plus company holidays
• Hybrid work model with flexibility
• Tuition reimbursement and student loan repayment assistance

EQUAL OPPORTUNITY EMPLOYER

We are an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request accommodation.

RECRUITING AGENCIES

HAVI does not accept agency resumes submitted by third-party vendors unless a valid agreement has been signed and the HAVI Talent Acquisition Team has granted authorization for submissions for a specified position. Please do not submit or forward resumes to our site, HAVI employees, or any other company location. HAVI is not responsible for any fees related to unsolicited resumes.

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