Lead Engineer AI/ML - Onsite

We are seeking a Machine Learning Engineer to join the Information Technology organization at our corporate office in Springfield, MO.

The Machine Learning Engineer designs, builds, tests, and optimizes machine learning systems that support enterprise AI initiatives across the business. This role is responsible for developing production-ready model code, inference logic, and reusable ML components that convert approved enterprise data into reliable operational signals, recommendations, automations, or insights.

This position works closely with AI leadership, Data Science, MLOps, Data Engineering, Product/Delivery, Security, Privacy, Store Operations, Merchandising, and other cross-functional partners to implement practical AI solutions. The role must balance model quality, latency, cost, privacy, maintainability, and operational usefulness.

This position requires working onsite in our Springfield, MO headquarters. Occasional travel to field locations may be required.

ESSENTIAL FUNCTIONS:

• Design, develop, and evaluate machine learning models and inference pipelines for enterprise AI use cases across retail, operations, merchandising, customer experience, supply chain, and corporate functions.
• Build production-quality Python code for model training, evaluation, preprocessing, postprocessing, inference services, and reusable model components.
• Partner with Data Scientists to define ground truth datasets, labeling requirements, evaluation metrics, confidence thresholds, and acceptable error tradeoffs.
• Partner with MLOps / Cloud ML Engineering to package, register, deploy, monitor, and optimize models in cloud, edge, or hybrid environments.
• Evaluate and select model architectures, pretrained models, fine-tuning approaches, and inference strategies appropriate for the business problem and operating environment.
• Prepare and transform approved structured and unstructured data for model development while following privacy, retention, and acceptable-use constraints.
• Build or integrate data labeling, sampling, augmentation, and validation workflows needed for model development and evaluation.
• Optimize inference performance for latency, cost, throughput, reliability, and deployment target.
• Implement model output schemas and event metadata structures in partnership with Data Engineering and API/application teams.
• Integrate model outputs with APIs, event streams, dashboards, reports, applications, or other approved enterprise presentation layers.
• Write automated tests for model code, preprocessing logic, inference services, schema contracts, and regression checks.
• Troubleshoot model failures caused by data quality, domain shift, operational changes, drift, or degraded source data.
• Document model assumptions, limitations, dependencies, reproducibility steps, evaluation results, and production readiness criteria.
• Support responsible AI practices, including PII minimization, privacy-aware design, model explainability where practical, and secure handling of approved enterprise data.
• Contribute to architecture decision records, model cards, technical runbooks, documentation, and reusable engineering standards.
• ALL OTHER DUTIES AS ASSIGNED.

EXPERIENCE/QUALIFICATIONS:

Minimum Degree Required: Bachelor's Degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, Computer Engineering, Applied Mathematics, or a related technical field, or equivalent experience.

• 8+ years of experience in software engineering, machine learning engineering, applied AI engineering, or production ML systems.
• 5+ years of hands-on experience building, training, fine-tuning, or deploying machine learning models in applied business environments.
• Strong proficiency in Python and modern machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, OpenCV, or equivalent tools.
• Experience with one or more ML domains such as natural language processing, forecasting, classification, recommendation systems, optimization, anomaly detection, multimodal AI, or generative AI.
• Experience building production-quality APIs, services, or batch/streaming inference components.
• Experience with Git, automated testing, code review, containerization, and collaborative engineering practices.
• Familiarity with model optimization and deployment formats or tooling such as ONNX, TensorRT, OpenVINO, quantization, batching, or similar techniques preferred.
• Familiarity with Azure Machine Learning, Azure AI services, Databricks, MLflow, or equivalent cloud ML platforms preferred.
• Familiarity with event-driven architectures, REST APIs, message queues, data lakes, and metadata/event pipelines preferred.
• Experience with distributed