Principal Technical Architect - AI/ML, Advanced Services

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done.

LEAD. STRATEGIZE. TRANSFORM.

SUMMARY:

Recognized expert and strategic technical advisor resolving critical, wide-ranging design matters for legacy modernization and Generative AI.

WHY THIS ROLE?

You act as the technical "North Star" for high-stakes migrations. You exercise wide latitude in determining objectives and approaches to critical assignments with high business impact.

WHAT YOU’LL DO:

• Develop reusable solution patterns, global reference architectures, and practice-wide white papers.

• Modernize legacy stacks (Netezza, Teradata, Exadata) into Snowflake AI workloads using deep architectural knowledge.

• Act as a trusted technical advisor to C-suite stakeholders, bridging business strategy with AI execution.

• Solve for LLM robustness, including managing prompt safety, hallucinations, and latency at enterprise scale.

• Gen AI Robustness: Architect enterprise-grade guardrails for LLMs, ensuring safety and performance.

• Legacy Transformation: Facilitate high-complexity data migrations from on-prem legacy systems into AI-ready cloud environments.

• Strategic Risk Management: Determine technical approaches for assignments that impact regional or global design success.

SNOWFLAKE-NATIVE TECH STACK:

Vector Data Types, Snowflake Cortex, Advanced LLM Frameworks, Global Asset Creation.

OUR IDEAL CANDIDATE WILL HAVE:

• Typically requires 12+ years of related experience with a Bachelor's; or 8 years with a Master's.

• Expert reputation in the AI/ML community and the Snowflake ecosystem.

• Visionary thinker with the ability to influence regional practice strategy and create regional frameworks.

• Adoption rate of authored assets; Performance in "must-win" strategic accounts; Regional practice influence.

• Strong executive communication: Ability to present complex AI/ML concepts and ROI to technical and business audiences.

Snowflake is growing fast, and we’re scaling our team to help enable and accelerate our growth. We are looking for people who share our values, challenge ordinary thinking, and push the pace of innovation while building a future for themselves and Snowflake.

How do you want to make your impact?

For jobs located in the United States, please visit the job posting on the Snowflake Careers Site for salary and benefits information: careers.snowflake.com

• Highlight hands‑on experience with Snowflake’s Data Cloud (Snowpark, Snowpipe, and the native Python/SQL UDFs) by sharing a GitHub repo that shows end‑to‑end AI/ML pipelines built directly on Snowflake, including performance benchmarks and cost‑optimization metrics.
• Prepare a concise “AI‑Native Thought‑Leadership” deck (3‑5 slides) that outlines a real‑world problem you solved by treating an LLM as a high‑trust collaborator—detail prompt engineering, evaluation loops, and how you iteratively “experiment‑and‑learn” to improve outcomes, mirroring Snowflake’s fast‑moving culture.
• During the technical interview, demonstrate low‑ego collaboration: when presented with a design challenge, first outline your initial architecture, then explicitly ask the interviewer for constraints or alternative viewpoints before refining the solution, showcasing your ability to integrate feedback quickly.
• Showcase expertise in distributed model serving and model‑as‑a‑service on cloud‑agnostic platforms (AWS, Azure, GCP) by describing a production‑grade deployment that leverages Snowflake’s external functions or stage‑based model storage, emphasizing security, scalability, and observability.
• When discussing compensation, reference Snowflake’s remote‑work allowance and the market premium for Principal Technical Architects in AI/ML (e.g., base + targeted RSU refresh ≈ 30‑40% of total). Frame your ask around “total impact value”—link expected outcomes (e.g., reducing data‑to‑insight latency by X% or enabling Y new AI products) to the equity component.