Job Description
AI Engineer (on-site) Ziosk - 3.1 Plano, TX Job Details 1 day ago Qualifications AI models Performance monitoring Regression testing implementation Software engineering Continuous Delivery (CD) implementation Engineering development testing Software deployment AI platforms (beyond public GPTs) Computational framework Prompt engineering Production systems Machine learning cloud services Machine intelligence Outlier detection Model deployment Version control systems APIs Monitoring system implementation A/B testing DevOps automation Machine learning libraries Model evaluation Machine learning frameworks MLOps Providing code feedback Full Job Description AI Engineer (On-site) - Plano, TX 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 Engineer to join our Enterprise Data department. Ziosk's AI bench is currently a team of one — and we're growing it. You will work alongside our Senior AI/ML Engineer on production AI/ML systems, and contribute to flagship AI products like the IPL Ad Optimizer and Contextual Intelligence engine.
This is hands-on engineering:
we ship to production and iterate from real telemetry. The Main Course - Responsibilities Build, train, evaluate, and deploy production AI/ML models alongside the Senior AI/ML Engineer — including the IPL Ad Optimizer and successor models. Own end-to-end development of agent products: requirements, architecture, prompt engineering, evals, deployment, and monitoring. Lead the Documentation Agent project and contribute to the QA Agent in partnership with QA Automation. Share the embedded POD on-call rotation for AI products; respond to model performance regressions, evaluate drift, and ship fixes. Define and run model evaluations — both offline (golden sets, regression suites) and online (shadow deployments, A/B tests, telemetry-driven iteration). Partner with Full-Stack Data BI Engineers to embed ML-powered features inside Dash applications, including anomaly detection, predictive metrics, and AI-generated narratives. Contribute to MLOps platform standards — model registry hygiene, CI/CD for models, monitoring, and drift detection. What You Bring To The Table - Qualifications Required 5+ years of professional software engineering experience, with at least 3 years building production AI/ML or LLM-powered systems. Strong Python proficiency and production experience with modern ML frameworks (PyTorch, TensorFlow, scikit-learn) and ML platforms (MLflow, Databricks ML, SageMaker, or equivalent). Hands-on experience building production LLM agents — prompt engineering, tool calling, and agent orchestration (LangGraph, AutoGen, Anthropic API, OpenAI API, or similar). Experience designing and running model evaluations, including classical ML metrics and LLM evals (golden sets, automated graders, A/B testing). Working knowledge of MLOps fundamentals:
model versioning, CI/CD for models, drift detection, monitoring, and rollback patterns. Strong software engineering fundamentals:
version control, testing, code review, and on-call experience in production AI systems. Preferred Direct production experience with Anthropic Claude API, OpenAI API, or other frontier LLM providers. Experience building agentic systems — multi-step reasoning, tool use, memory, and error recovery. Familiarity with Databricks ML, Unity Catalog ML lineage, and lakehouse-native ML patterns. Background in restaurant, retail, hospitality, or other guest-facing applications of ML such as recommendation, personalization, or demand forecasting. Active use of AI-assisted development tooling (Cursor, Claude Code, GitHub Copilot) in your own engineering work. Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, or equivalent practical experience. 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 daily to Plano, TX. No agencies or third-party recruiters, please .
