AI/ML Developer

Description

Onsite working

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.