Senior AI/ML Architect, Applied Field Engineering

Snowflake1 weeks ago

Location

US-WA-Seattle-Remote

Workplace

Remote

Type

Full Time

Salary

USD 165,000 – 216,562

Level

Senior

Role

ML Engineer

Posted

Mar 2, 2026

Full TimeRemoteSenior

The role

Summary

Snowflake is seeking a Senior AI/ML Architect to serve as the technical expert for AI and ML solutions on the Snowflake AI Data Cloud platform. This role involves working with enterprise customers across the Americas to design, architect, and implement AI solutions while collaborating with cross-functional teams to drive product roadmaps and customer adoption.

What you'll do

Technical AI/ML Leadership: Serve as the technical expert positioning Snowflake's AI and ML features to enterprise customers across the Americas

Proof of Concept Development: Partner with account teams to scope, drive, and deliver successful POCs demonstrating Snowflake's AI capabilities

Product Roadmap Influence: Collaborate with product and engineering teams to influence AI and ML roadmaps based on customer feedback and market needs

Content Creation and Scaling: Publish technical content including blog posts, conference presentations, notebooks, and demos to scale team impact

Sales Asset Development: Create, tailor, and maintain AI/ML selling assets including customer presentations, demonstrations, and success stories

Executive Communication: Deliver technical readouts and business value presentations to executive stakeholders

Customer Architecture Design: Design and architect AI solutions built on the Snowflake AI Data Cloud platform for enterprise use cases

What we look for

Technical

Machine Learning Experience5+ years building and deploying ML and generative AI solutions in cloud environments

Generative AI ExpertiseDeep knowledge of RAG, few-shot learning, prompt engineering, and fine-tuning techniques

Python ProficiencyExpert-level Python skills with ML packages including LangChain, pandas, sklearn, and PyTorch

Data Engineering ToolsExperience with dbt, Airflow, Spark, and other data pipeline technologies

Presentation SkillsStrong ability to present to both technical and executive audiences through various formats

Education

Bachelor's DegreeRequired in computer science, engineering, mathematics, or related field

Master's DegreePreferred in computer science, engineering, mathematics, or equivalent experience

Experience

Cloud ML Deployment5+ years of hands-on experience building and deploying machine learning solutions in cloud environments

Enterprise AI SolutionsExperience operationalizing AI use cases like interactive chat applications and text processing

Large-scale InfrastructureWorking knowledge of IaaS platforms like AWS, Azure, or GCP

Database TechnologyKnowledge of large-scale database systems and data warehousing platforms

Skills

Required skills

Python ProgrammingExpert-level proficiency in Python with focus on ML and data processing

Machine Learning FrameworksHands-on experience with PyTorch, scikit-learn, pandas, and LangChain

Generative AI TechniquesDeep knowledge of RAG, prompt engineering, few-shot learning, and fine-tuning

Data EngineeringProficiency with dbt, Airflow, Spark, and modern data pipeline tools

Cloud PlatformsExperience with AWS, Azure, or GCP infrastructure and services

Technical PresentationStrong ability to communicate complex technical concepts to diverse audiences

Nice to have

Snowflake Platform1+ years of practical experience with Snowflake data cloud platform

LLM Ecosystem ToolsWorking knowledge of LlamaIndex and other open-source LLM packages

Enterprise Database SystemsExperience with large-scale databases like Teradata, Netezza, or Greenplum

Solution ArchitectureExperience designing and implementing enterprise-scale AI/ML architectures

Customer-Facing ExperienceBackground in technical sales, consulting, or customer success roles

Compensation & benefits

Salary

USD 165,000 – 216,562 (annual)

Stock options

Available

Benefits

Competitive Salary

Annual compensation range of $165,000 - $216,562 based on experience and location

Stock Options

Equity participation in Snowflake's growth as a publicly traded cloud computing company

Health Insurance

Comprehensive medical, dental, and vision coverage

Professional Development

Conference attendance, training, and continuous learning opportunities

Remote Work

Fully remote position with flexible work arrangements

Innovation Culture

Work in a fast-growing company focused on impact, innovation, and collaboration

Interview process

1
Initial Screening — Phone or video call with recruiter to discuss background and role fit
2
Technical Assessment — Deep-dive technical interview covering ML concepts, Python coding, and AI architecture
3
Case Study Presentation — Present a technical solution or POC scenario demonstrating customer-facing skills
4
Cross-functional Interviews — Meetings with product, engineering, and field teams to assess collaboration fit
5
Final Executive Interview — Discussion with senior leadership focusing on strategic thinking and company alignment