AI Infrastructure Architect

Project Role : AI Infrastructure Architect

Project Role Description : Architect and build custom Artificial Intelligence (AI) infrastructure/hardware solutions. Optimize AI infrastructure/hardware performance, power consumption, cost and scalability of computational stack. Advise on AI infrastructure technology and vendor evaluation, selection and full stack integration.

AI Powered Tech Talent
As a Senior Engineer in AI Infrastructure Architecture for Snowflake , you will own significant portions of the end-to-end architecture and engineering of optimized data and AI infrastructure for production machine learning and AI-enabled applications. You will design scalable warehouses, Snowpark workloads, AI-ready data/feature pipelines, model-enablement patterns, automation and operational controls that align with client standards, SLAs, security, compliance and cost-efficiency expectations. You will bring industry experience across enterprise AI adoption, platform modernization, regulated data workloads, FinOps and production reliability, while mentoring engineers and partnering with architects to translate business requirements into robust Snowflake-based AI infrastructure solutions.

Key Responsibilities
Own end-to-end architecture and design of optimized Snowflake data and AI infrastructure, including warehouses, Snowpark workloads, secure data architecture, AI-ready feature/data pipelines, model integration and AI application enablement.
Design and tune scalable Snowflake warehouses, tasks, streams, Snowpark services, Cortex/AI capabilities, Streamlit applications and cloud integrations, including compute sizing, query optimization, governance, access controls and high-throughput data access design.
Serve as an authoritative AI infrastructure expert on Snowflake, applying deep knowledge of Snowflake Data Cloud capabilities, data/AI application patterns, governance, security and cost levers.
Develop and evaluate architecture alternatives, weighing trade-offs across compute, networking, storage, orchestration, model serving, observability, security, compliance, cost and operational complexity.
Lead architecture assessments and reviews of existing and proposed environments, identifying gaps, risks, bottlenecks and optimization opportunities, and recommending remediation actions.
Drive architecture decision-making by documenting rationale, trade-offs, assumptions and dependencies so decisions are transparent, defensible and aligned with business SLAs and standards.
Define and maintain AI infrastructure roadmap inputs, capacity planning models, scaling strategies, cost forecasts and performance improvement opportunities.
Design deployment, automation and CI/CD strategies for reliable, repeatable and scalable releases of AI systems, models, data pipelines and platform components into production.
Establish AI monitoring and observability practices across InfraOps and MLOps, including SLAs, SLOs, alerting, performance/cost tracking and continuous optimization.
Integrate AI/ML systems into enterprise environments while ensuring interoperability, security, compliance, regulatory alignment and adherence to client standards.
Collaborate with clients, stakeholders, architects and engineering teams to align infrastructure decisions with business outcomes and translate requirements into actionable architecture standards.
Set technical direction for workstreams, mentor engineers, review designs/code and promote engineering best practices across the team.