AI ML Engineer (on-site)

AI/ML III. Engineer (on-site)

Welcome to Ziosk, where we empower restaurants to focus on what matters most: the guest experience!

Have you ever used a tablet to pay at a restaurant? We pioneered the pay-at-the-table concept and we’re cooking up a plan to transform the restaurant industry. Our recipe for success has been adapting and growing to exceed the needs of our clients, such as Olive Garden, Texas Roadhouse, Chili’s and more – helping them create an experience that keeps guests coming back. Today we have a full menu of solutions, from hardware to software to cloud-based and AI driven products, all focused on helping them create the best guest experience possible to grow their bottom line.

Our secret sauce? Our people! Every day, they’re cooking up bold solutions, making Ziosk the leading pay-at-the-table provider in the industry.

Want a seat at our table? Ziosk is looking for a highly experienced AI/ML Engineer to join our innovative team dedicated to improving our products and services through advanced AI technologies. You will work closely with data scientists, engineers, and product managers to design, implement, and deploy scalable artificial intelligence solutions.

The Main Course – Responsibilities 

• Design and develop machine learning algorithms and models to solve complex problems and enhance user experiences

• Design, build, and deploy ML pipelines using Azure Machine Learning Service (AzureML).
• Collaborate with data scientists, analysts, and engineers to transform prototypes into production-grade solutions.
• Operationalize models using MLOps best practices, including CI/CD integration with Azure DevOps or GitHub Actions.

• Collaborate with data engineers to build and maintain robust data and model pipelines using tools like Microsoft Fabric, Azure, and Databricks
• Perform data exploration and feature engineering to boost model accuracy and efficiency
• Monitor and retrain models as necessary to handle data drift or concept drift
• Monitor and evaluate system performance, applying tools such as Azure Monitor, Application Insights, CosmosDB, and Splunk
• Continuously improve existing ML systems for performance and stability
• Ensure high standards of data quality, lineage, and governance, with traceability built into the pipeline design
• Strong foundation in data engineering concepts and tools (e.g., Apache Spark, Databricks, Azure Data Factory, MLFlow)
• Leverage Azure Cognitive Services and OpenAI on Azure where applicable
• Experience scaling ML pipelines for performance in cloud environments
• Knowledge of data governance frameworks and observability best practice

What You Bring to the Table – Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
• 3 years of experience building ML models, with at least 2 years using Azure cloud services.
• Proficiency in Python, including libraries like scikit-learn, pandas, NumPy, TensorFlow, or PyTorch.
• Hands-on experience with:
  • Azure Machine Learning (AzureML)
  • Azure Data Lake / Azure Blob Storage
  • Azure Databricks or Spark on Azure Synapse
  • Azure DevOps / GitHub Actions
• Strong understanding of machine learning techniques such as classification, regression, clustering, and time series forecasting.
• Experience deploying models via Azure ML endpoints, Azure Functions, or AKS.
• Solid understanding of containerization using Docker, and optionally Kubernetes.
• Familiarity with Azure Cognitive Service and Azure Open AI
• Knowledge of responsible AI and model interpretability tools (e.g., SHAP, LIME, Fairlearn) is a plus
• Azure Certifications (e.g., Azure Data Scientist Associate (DP-100), AI Engineer Associate (AI-102) are a plus

• Contributions to open-source projects or active participation in the AI community.

Ziosk is an Equal Opportunity employer offering competitive benefits and compensation. Candidates must be eligible to work in the U.S. and be able to commute to Plano, TX daily. Applicants must be authorized to work for any employer in the U.S. No agencies or third-party recruiters, please.