AI/ML Developer

POSITION OVERVIEW : Data Scientist Department: Data and AI Team Reports To: Data Science Manager Location: Hybrid (Auburn Hills) Experience Level: 7+ years of overall IT or software development experience, including 4+ years of hands-on AI/ML solution development experience. POSITION GENERAL DUTIES AND TASKS : Roles and Responsibilities Design, develop, test, deploy, and support scalable AI and machine learning solutions across enterprise platforms. Translate business requirements, operational workflows, and solution architecture into detailed technical designs and production-ready implementations. Develop high-quality Python code, reusable AI/ML services, APIs, microservices, automation workflows, and integration components. Build, train, evaluate, tune, and deploy machine learning and deep learning models using appropriate algorithms and frameworks. Develop Generative AI solutions, including conversational AI, Retrieval-Augmented Generation (RAG), intelligent agents, automation workflows, and multi-agent solutions where appropriate. Implement agent collaboration patterns supporting sequential, parallel, event-driven, conditional, and human-in-the-loop interactions. Integrate AI/ML solutions with enterprise applications, APIs, event-driven services, structured and unstructured data sources, and knowledge repositories. Implement secure authentication, authorization, identity propagation, secrets management, and access controls across AI application and integration layers. Apply Responsible AI and enterprise governance requirements covering data privacy, prompt security, model safety, explainability, compliance, auditability, and human approval workflows. Develop and maintain MLOps and LLMOps pipelines for automated testing, deployment, model and prompt versioning, monitoring, and lifecycle management. Implement evaluation frameworks and quality measures for model accuracy, relevance, groundedness, hallucination risk, safety, latency, reliability, scalability, and cost. Monitor production AI/ML solutions for application health, model performance, data or model drift, observability, and operational issues.

Artificial Intelligence (AI), Data Science, Artificial Intelligence (AI), Software Development, Information Technology Software, Machine Learning, Multiplatform/Cross-Platform, Test Plan/Schedule, Technical/Engineering Design, Requirements Management