Software Engineer, Applied AI

Snowflake is building a high-impact team to help the world’s most innovative companies unlock the power of AI, placing the role at the intersection of product, engineering, and customer success, building production-grade AI systems using Snowflake AI Platform, Cortex, and native LLM capabilities.

Minimum Qualifications:

• Bachelor’s degree in Computer Science, Engineering, or related technical field, or equivalent practical experience.
• 3+ years of professional software engineering experience.
• Passion for tackling complex and ambiguous technical challenges using cutting-edge AI research.
• Hands-on experience building and shipping LLM-based or ML applications.
• Proven experience with data modeling, ETL/ELT development, and performance tuning.
• Advanced proficiency in Python, Java, C++, or other backend programming languages with scripting and automation of data workflows.
• Excellent problem-solving and communication skills.
• Ability to thrive and adapt quickly in a fast-paced, dynamic environment focused on Generative AI.

Preferred Qualifications:

• Experience building production applications using LLMs, including RAG and agentic workflows.
• Hands-on experience with the MLOps lifecycle: model deployment, monitoring, and evaluation in cloud environments (AWS, Azure, GCP).
• Strong understanding of data warehousing principles, architecture, and best practices.
• Experience in a customer-facing role (e.g., solutions architect).
• Startup experience.

• Architect, build, and deploy enterprise-grade AI solutions including sophisticated AI agents from prototype to production.
• Build and iterate on AI pipelines and agents, contributing to evaluation frameworks and observability infrastructure.
• Rapidly design, iterate, and ship high-quality code and ML pipelines using Python and SQL.
• Own full lifecycle of AI solution implementation including deployment, monitoring, and optimization in secure large-scale production environments.
• Partner with customer data science and engineering teams as a technical expert and trusted advisor.
• Architect and implement rigorous data validation, maintain SLA observability, and manage complex system interdependencies.
• Collaborate cross-functionally with Snowflake’s Product and Engineering teams to provide real-world feedback impacting AI platform development.
• Progressively take on larger ownership from component to customer engagement as engineering judgment and impact grow.