Engineer, Enterprise AI

Overview Due to growth, we are adding a Engineer, Enterprise AI to our team! The Engineer, Enterprise AI is responsible for designing, building, and operationalizing enterprise AI capabilities that enable the scalable adoption of Artificial Intelligence and Agentic AI across the organization. Working closely with the Enterprise AI Architect, this role translates enterprise AI architecture, patterns, and governance standards into implementable solutions and reusable technical components while partnering with Enterprise Architecture, Platform Engineering, Data Engineering, and Product teams to develop AI services, integrations, and operational frameworks that support generative AI, machine learning, and agentic workflows. The Engineer ensure AI solutions are implemented in alignment with enterprise architecture standards, security requirements, and regulatory compliance while enabling teams across the organization to safely and effectively leverage AI technologies to deliver business value. Is this you? Find out more below! Responsibilities How do I make an impact on my team? - Design, build, and maintain enterprise AI solutions and services that enable the scalable adoption of generative AI, machine learning, and agentic AI capabilities across the organization. - Implement enterprise AI architecture patterns and technical standards defined by the Enterprise AI Architect, translating reference architectures and design patterns into production-ready solutions. - Develop and support AI integration patterns that connect AI capabilities with enterprise applications, APIs, data platforms, and cloud services. - Build and maintain AI-enabled services and components, including model interfaces, orchestration services, agent frameworks, and reusable AI tooling for enterprise teams. - Support the implementation of Agentic AI workflows, including orchestration, tool integration, context management, and human-in-the-loop capabilities. - Implement and maintain AI operational capabilities (MLOps/LLMOps) including model deployment pipelines, monitoring, observability, lifecycle management, and operational controls. - Ensure AI solutions align with enterprise AI governance, responsible AI standards, and regulatory requirements, including data privacy, auditability, and model transparency. - Support AI platform development and enablement, working with Platform Engineering and Data Engineering teams to deploy, scale, and maintain enterprise AI infrastructure and tooling. - Other duties as assigned. Qualifications What our team expects ** from you?** Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Information Systems, or a related technical field, or equivalent work experience, required. Certification/Licenses: - AWS Certified Solutions Architect -- Associate, AWS Machine Learning -- Specialty, or equivalent cloud-based AI/ML certification preferred. - MLOps, AI platform engineering, or machine learning engineering certification preferred. - Responsible AI, AI governance, or AI ethics training/certification preferred. 6-8 years of experience in software development, platform engineering, or machine learning engineering, including a minimum of 2-4 years of hands-on experience developing, integrating, or operationalizing Artificial Intelligence and Machine