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Snowflake is seeking a Forward Deployed Engineer for its Cortex AI team to architect, build, and deploy enterprise-grade AI solutions for strategic customers. This is a hands-on technical role at the intersection of product, engineering, and customer success, where you will own the end-to-end lifecycle of AI solution implementation—from prototype to production—directly solving complex business challenges at scale.
Key responsibilities include architecting and deploying sophisticated AI agents and data-intensive systems using Snowflake's AI Platform, Cortex, and native LLM capabilities. You will rapidly design, iterate, and ship high-quality code and ML pipelines, translating ambiguous business objectives into robust, scalable solutions using Python and SQL. The role requires productionizing AI systems in secure, large-scale production environments while maintaining strict SLA observability and managing complex system interdependencies.
You will serve as a strategic technical advisor to customer data science and engineering teams, leveraging your expertise to guide them on best practices for AI adoption. Cross-functional collaboration with Snowflake's Product and Engineering teams is essential, as you will share real-world customer feedback that directly influences the platform's evolution.
Required qualifications include a Bachelor's degree in Computer Science, Engineering, or related technical field (or equivalent practical experience), plus 3+ years of professional software engineering experience. You must demonstrate advanced proficiency in Python, hands-on experience with data modeling, ETL/ELT development, and performance tuning. Experience building, evaluating, and tuning ML applications and data-intensive pipelines is essential, along with familiarity with core data science libraries (pandas, numpy, Snowpark).
Preferred qualifications include proven experience productionizing LLM applications (especially RAG and agentic workflows), MLOps lifecycle knowledge (model deployment, monitoring, evaluation in cloud environments like AWS, Azure, or GCP), strong understanding of data warehousing principles, customer-facing solutions architecture experience, and startup background.
