Snowflake AI Engineer \\Job Title: Snowflake AI Engineer\\ \\Location:\\ Remote \\Contract Duration:\\ 6 months \\Interview Process:\\ 2 rounds \\ Qualifications:\\ - Prior professional experience working at an asset manager, investment manager, hedge fund, or closely related buy-side investment firm. - Working knowledge of investment management concepts and data: securities master/, 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 ). - Experience building and deploying machine learning models or AI applications in production environments. - Familiarity with cloud platforms (AWS, Azure, or GCP) 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. \\Application Instructions:\\