Applied AI Engineer

About the Role

The Applied AI Engineer turns ideas into production-ready AI solutions spanning feasibility, data engineering, model development, and integration. Operating within the AI Strategy team, this role accelerates Little Caesars' AI future by rapidly prototyping and piloting high-impact use cases. This role will partner closely with Data Engineering, Architecture, Product, and Application Development teams to ensure the team can rapidly prototype within a secure environment. The ideal candidate can work across the stack, with experience in platforms such as Databricks and the ability to move solutions from data pipelines and experimentation through APIs, user experiences, and production support.

Responsibilities

• Design, build, and deploy end-to-end AI solutions that span data ingestion, transformation, model development, evaluation, deployment, and application integration
• Develop scalable data pipelines and feature engineering workflows using modern data platforms, including Databricks
• Build and productionize machine learning and generative AI solutions that address prioritized business use cases
• Create APIs, services, and application components that embed AI capabilities into internal tools, workflows, and user-facing experiences
• Work with architecture and platform teams to establish reusable patterns for model serving, orchestration, monitoring, and secure deployment
• Collaborate with product, business, and technical stakeholders to translate requirements into practical AI-enabled solutions with measurable impact
• Support proof-of-concepts, pilots, and production implementations while balancing speed, scalability, maintainability, and responsible AI practices
• Partner with data governance, security, and infrastructure teams to ensure AI solutions meet enterprise standards for privacy, reliability, and compliance
• Stay current on emerging AI engineering practices, frameworks, and tools, and recommend technologies that improve delivery speed and solution quality
• Mentor teammates and contribute to the evolution of engineering standards, reusable components, and AI platform capabilities

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or a related technical field, with experience building and deploying AI or machine learning solutions in production environments. Equivalent experience may be considered in lieu of formal degree.
• 2+ years of experience developing end-to-end AI solutions, including data pipelines, model training, evaluation, deployment, and application or API integration
• Strong programming skills in Python and SQL, with the ability to build maintainable, production-quality code and integrate open-source AI frameworks
• Hands-on experience with data engineering concepts such as data modeling, transformation, orchestration, and feature preparation for AI workloads
• Experience building services, APIs, or lightweight applications that operationalize AI capabilities for end users or internal teams
• Experience with machine learning, generative AI, and applied AI patterns such as retrieval, recommendation, forecasting, optimization, or intelligent assistants
• Experience with secure development practices, data privacy controls, and operational monitoring for AI and data solutions

Preferred Skills

• Knowledge of cloud and hybrid data architectures and how storage, compute, and governance choices influence AI solution design
• Understanding of modern product and engineering practices such as Agile, DevOps, CI/CD, MLOps, and responsible AI delivery
• Experience with Databricks, notebooks, model serving, MLflow, and related cloud services such as Azure AI Foundry, Microsoft Fabric, or Azure Data Factory

Working Conditions

This position may require minimal travel to vendor locations, assessment at restaurants and Ilitch Companies facilities, data centers or seminars/networking.

Competencies

• Demonstrated comfort in ambiguity and ability to work in a fast-paced continuous delivery environment
• Ability to communicate technical concepts, architecture decisions, and solution tradeoffs clearly to both technical and non-technical stakeholders
• Practiced at solving ambiguous business problems through structured thinking, iterative delivery, and cross-functional collaboration
• Excellent written and verbal communication skills with the ability to document technical solutions and support collaborative delivery