Principal AI Architect

Role overview

Technical ownership and delivery

• Prioritize use-case roadmaps by business value and delivery feasibility, and set realistic plans.
• Translate business and policy needs into scoped technical work.
• Communicate scope, timelines, and resourcing trade-offs; recommend additional resources when workstream scale requires them.

Solution architecture and engineering

• Design the data ingestion, storage, and quality foundation for prioritized AI use cases.
• Select AI/ML methods such as text classification, machine translation, retrieval and similarity search, LLM assistants or chatbots, forecasting, and decision-support scoring.
• Design integrations so AI outputs are production-ready and usable in existing business workflows.
• Build priority models and pipelines, and direct shared or contracted engineering support.
• Establish appropriately scaled MLOps practices for versioning, CI/CD, and monitoring.
• Validate models against agreed success metrics and provide evidence for milestone sign-off.

Governance and risk

• Align AI components with responsible-AI principles, data governance, and privacy requirements.
• Build explainability and audit logging into solutions by design.
• Identify and escalate technical risks, gaps, and scope trade-offs to stakeholders and leadership.

Required qualifications

• 8–11 years of AI/ML engineering and architecture experience.
• Experience owning AI solution delivery end to end, from design through production, in a lead architect or principal engineering capacity.
• Working knowledge of a range of AI/ML areas, including NLP and text classification, machine translation, retrieval or vector similarity search, LLM assistants, and forecasting.
• Practical experience designing and operating ETL/ELT or lakehouse-style pipelines for AI use cases.
• Strong stakeholder communication, scoping, and prioritization skills; able to translate business needs into technical designs and manage delivery trade-offs.
• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.

Advantageous qualifications

• Experience in regulated, public-sector, or specialized-domain environments is an advantage.
• An advanced degree is a plus.