Snowflake AI Engineer

Required Qualifications

• Prior professional experience working at an asset manager, investment manager, hedge fund, or closely related buy-side investment firm — this is a firm requirement, not a nice-to-have
• Working knowledge of investment management concepts and data: securities master/reference data, portfolio holdings and performance, market data, benchmarks, and/or investment research workflows
• Understanding of the regulatory, compliance, and data governance considerations specific to investment management (e.g., MNPI/information barriers, data lineage, model risk management)
• 4+ years of experience in software engineering, data engineering, or ML engineering roles
• Hands-on production experience with Snowflake (data modeling, performance tuning, Snowpark, security/role-based access)
• Strong programming skills in Python (SQL fluency required as well)
• Experience building and deploying machine learning models or AI applications in production environments
• Familiarity with cloud platforms (AWS, Azure, or Google Cloud Platform) and modern data architecture patterns
• Experience with version control, CI/CD, and software engineering best practices
• Strong communication skills and ability to work cross-functionally with investment professionals (portfolio managers, analysts, traders) as well as technical stakeholders

Preferred Qualifications

• Experience with LLM application development (prompt engineering, RAG architectures, agentic workflows, vector databases such as Pinecone/Weaviate/Snowflake Cortex Search)
• Experience with orchestration tools (Airflow, dbt, Dagster)
• Background in financial services, asset management, or another regulated industry
• Exposure to MLOps tooling (MLflow, Kubeflow, SageMaker, etc.)
• Bachelor''s or advanced degree in Computer Science, Engineering, Data Science, or related field