AI Infrastructure Architect

About the job

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.

Requirements

• Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related engineering field.
• Minimum 4 years of experience coding, building, monitoring, troubleshooting, designing, and operating AI/ML infrastructure, cloud platforms, data platforms, model deployment pipelines, or large-scale engineering solutions.
• Strong understanding of AI/ML concepts and the computing infrastructure required to deploy, run, and optimize production AI workloads.
• Minimum 4 years of proficiency in programming or scripting languages such as Python, Java, C++, Bash, PowerShell, or equivalent engineering languages.
• Experience with data pipeline and workflow management tools such as Apache Airflow, Kubeflow, managed orchestration services, or platform-native workflow tooling.
• Strong problem-solving skills and ability to work in a fast-paced engineering or client delivery environment.
• Excellent communication, collaboration, and stakeholder alignment skills.
• Minimum 4 years of experience in AI/ML infrastructure engineering or related roles on a hyperscaler or enterprise platform for deploying large-scale solutions.
• Proven experience leading AI projects or engineering workstreams and managing priorities across multiple initiatives.
• Demonstrated experience evaluating and selecting AI technologies, frameworks, cloud services, and architecture patterns.
• Strong hands-on experience with Snowflake warehouses, databases, schemas, secure data sharing/access controls, Snowpark, Cortex/AI capabilities, Streams/Tasks, Streamlit, and cloud ecosystem integrations.
• Experience architecting scalable data and feature pipelines, AI application integrations, model-enablement patterns, governance, query/warehouse optimization, and secure data access.
• Strong working knowledge of SQL, Python, dbt/Terraform, CI/CD, DataOps, MLOps/AI enablement patterns, observability, and incident response practices.
• Ability to optimize Snowflake AI infrastructure for performance, cost, scalability, security, reliability, and compliance.
• Experience producing architecture decision records, reference implementations, standards, runbooks, and reusable platform patterns.