Principal AI Architect

Job Description

Overview:
The Principal AI Architect is a senior technical leadership role responsible for defining and governing enterprise-wide AI, GenAI, and MLOps architecture. This role sets the long-term vision, reference architectures, and reusable patterns for scalable, secure, and responsible AI adoption across the organization. This role combines strong hands-on engineering with strategic innovation to design, prototype, and deliver intelligent, data-driven solutions that power analytics, machine learning, and next-generation AI applications.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances.

If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to  To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

• ○ Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• ○ 10+ years of experience in enterprise architecture, data platforms, or distributed systems.
• ○ Deep expertise in AI/ML architecture, including GenAI and LLM-based systems.
• ○ Strong experience designing MLOps platforms and enterprise AI foundations.
• ○ Proven experience architecting solutions on AWS.
• ○ Strong understanding of cloud security, networking, and governance.
• Proficiency in Python, SQL, and modern data frameworks (e.g., Databricks, Airflow, Snowflake, Vertex AI).