Full Time| Monday-Friday 8:00AM-4:30PM| Weekly Earned Wage Access is an option for this position. 60/40 onsite/remote

Job Purpose or Goals:

The AI architect defines the technical architecture for enterprise‑level AI systems and their integrations, ensuring they are designed for scalability, security, and high performance. They also play a critical role in enhancing system reliability and cost efficiency, ensuring that all components work cohesively in production environments. Success in this role requires a hands-on, collaborative mindset, the ability to thrive in ambiguity, and the interpersonal skills to build trust across business and technical teams.

Tasks:

1. Architect end-to-end AI systems and pipelines, ensuing each stage integrates securely, efficiently, and at enterprise scale. 2. Select technologies for storage, transit, computing, and orchestration by evaluating cloud services, distributed compute frameworks, and infrastructure components that best support long-scale AI workloads and long-term maintainability. 3. Establish MLOps standards and best practices to streamline development, automated deployment, and maintain consistency across the environment. 4. Ensure compliance, monitoring, and governance alignment by defining guardrails around data access and regulatory adherence. 5. Optimize compute and cost by making decisions about resource provisioning, model scaling, and infrastructure tuning. 6. Guide engineering teams on architecture implementation, providing technical direction, architectural patterns, and hands-on support to ensure solutions are built according to defined standards. 7. Produce documentation and reusable patterns by creating clear technical guides that accelerate delivery and promote consistency across projects. 8. Performs other duties as assigned.

Job Requirements:

Bachelor's degree in computer science or related field and 4 years relevant professional experience 7+ years in software/ML or AI engineering or architecture Experience with cloud ML & AI tools Expertise in model deployment and MLOps, AI & Orchestration Strong distributed systems knowledge Bachelor's degree in computer science or related field and 4 years relevant professional experience 7+ years in software/ML or AI engineering or architecture Experience with cloud ML & AI tools Expertise in model deployment and MLOps, AI & Orchestration Strong distributed systems knowledge 1. Architect end-to-end AI systems and pipelines, ensuing each stage integrates securely, efficiently, and at enterprise scale. 2. Select technologies for storage, transit, computing, and orchestration by evaluating cloud services, distributed compute frameworks, and infrastructure components that best support long-scale AI workloads and long-term maintainability. 3. Establish MLOps standards and best practices to streamline development, automated deployment, and maintain consistency across the environment. 4. Ensure compliance, monitoring, and governance alignment by defining guardrails around data access and regulatory adherence. 5. Optimize compute and cost by making decisions about resource provisioning, model scaling, and infrastructure tuning. 6. Guide engineering teams on architecture implementation, providing technical direction, architectural patterns, and hands-on support to ensure solutions are built according to defined standards. 7. Produce documentation and reusable patterns by creating clear technical guides that accelerate delivery and promote consistency across projects. 8. Performs other duties as assigned.