Sr. Staff, Data Science & Applied AI

Hands-on experience supporting Generative AI and ML workloads on enterprise cloud platforms, including AWS and Snowflake

Strong working knowledge of LLM-based systems, including model hosting, inference optimization, and integration into cloud-native architectures.

Experience enabling RAG pipelines and GenAI applications through secure data access patterns, vector search, and enterprise data integration.

Familiarity with embedding models, semantic search, and vector storage technologies, including OpenSearch vectors and Snowflake Cortex Search.

Understanding of LLM evaluation considerations within platform architecture, including performance monitoring, cost optimization, and quality metrics.

Experience designing MLOps and LLMOps architectures for model deployment, monitoring, retraining, and lifecycle governance.

Strong expertise in AWS cloud architecture, including VPC, IAM, S3, ECS/EKS, SageMaker, Bedrock, and observability tooling.

Hands-on experience with infrastructure as code, CI/CD pipelines, and containerized AI workloads (Docker, Kubernetes).

Working knowledge of Snowflake architecture, including Snowpark, Snowpipe, Streams & Tasks, and Cortex AI.

• Deep understanding of Responsible AI, data security, privacy controls, and governance requirements for enterprise AI platforms.