AI Engineer

PAR Technology Corporation is a leader in restaurant technology, providing innovative solutions to empower brands worldwide. They are seeking an AI Engineer to leverage AI capabilities and restaurant data to enhance user experiences through intelligent product features and workflows.

Responsibilities:

• Design and build user-facing AI capabilities that simplify decision-making, surface insights, and enable faster action for operational teams
• Create and maintain multi-step LLM workflows (chains) that combine prompts, tools, and data to execute complex tasks—enabling conversational agents, data-powered assistants, and automation flows that deliver measurable process improvements
• Use retrieval-augmented generation (RAG) to combine internal data with LLMs and generate context-aware, trustworthy results
• Deliver features that set PAR apart from the competition and provide tangible value to users—leading to greater product adoption and customer retention

Requirements:

• 5+ years' experience as a software engineer, ideally within a SaaS or data-centric product environment
• Proficiency with LLM platforms and APIs (e.g., OpenAI, Azure OpenAI)
• Familiarity with prompt engineering, embedding models, and vector databases
• Experience designing and implementing multi-step AI workflow chains (e.g., LangChain, Semantic Kernel, custom pipelines)
• Strong data skills, including proficiency with SQL and structured datasets in production
• Understanding of cloud-based orchestration tools like Azure Data Factory or equivalents
• Experience taking AI features from prototype to production with attention to scalability, cost-efficiency, and observability
• Strong communication skills and ability to work cross-functionally with product, engineering, and data teams
• Recognized as an internal driver of AI strategy and implementation, setting standards and best practices for trustworthy, scalable AI
• Experience working with data-rich applications, especially in retail, hospitality, or restaurant operations
• Exposure to data visualization, analytics platforms, or reporting tools
• Experience with traditional machine learning workflows (e.g., scikit-learn, TensorFlow, PyTorch)