Generative AI Engineer

Semantic Layer & Metrics Reporting (initial focus) Design, configure, and maintain the Snowflake semantic layer (e.g. Cortex Analyst semantic views), modeling business logic and metrics definitions accurately against client requirements Build and deliver metrics and reporting for clients off of the configured semantic layer Work directly with client stakeholders to define metrics requirements, validate outputs against business logic, and resolve discrepancies Write efficient, production-grade SQL and Python to support semantic layer configuration, metrics pipelines, and reporting delivery GenAI & Agentic Systems (as the role evolves) Design and implement retrieval-augmented generation (RAG) systems, agentic workflows, and MCP-based tooling that draw on the semantic layer as their source of truth Build and maintain evaluation harnesses to test and monitor AI/agent quality and performance Apply sound judgment on model selection, prompting strategy, and system design tradeoffs Own technical architecture and model strategy decisions for AI systems built on the Snowflake platform Serve as the senior-most AI technical voice on client engagements, helping shape technical direction and roadmap for AI components, without owning the engagement or its reporting Work directly with client stakeholders to scope AI/ML use cases, translate business requirements into technical solutions, and set realistic expectations on scope and timeline Implement and configure solutions within client environments, adapting the platform to client-specific data and constraints Serve as the technical point of contact for clients during implementation, including troubleshooting issues live with client teams Manage client relationships and expectations with the same rigor applied to the technical work itself Cross-Functional Collaboration Partner with product, data science, and operations teams to move AI/ML work from prototype to production Communicate technical tradeoffs clearly to both technical and non-technical audiences, internal and client-facing Contribute to team best practices for AI engineering and client implementation work What you bring to the team 8+ years of software/AI engineering experience, including hands-on design of AI/ML systems Deep, hands-on experience with Snowflake, including Snowpark, Snowflake Cortex, and Snowflake ML Hands‑on experience building and maintaining semantic layers on Snowflake (e.g. Cortex Analyst semantic views), including modeling business logic and metrics for AI/agent consumption Experience delivering metrics and reporting off of a semantic layer for clients, including validating outputs against business logic Demonstrated experience designing and building GenAI/agentic systems: RAG, MCP-based tooling, agent orchestration patterns, and evaluation harnesses A proven track record working directly with external clients or customers in an implementation, forward‑deployed, or professional‑services capacity, with specific engagements you personally owned, not only internal engineering experience Demonstrated ability to operate as the senior technical voice on ambiguous problems, with sound judgment on AI technical direction, without needing close direction Strong Python engineering skills, including API design and delivery of production AI/ML pipelines Strong communication skills, with demonstrated ability to translate technical tradeoffs for non‑technical, client‑facing audiences Comfort operating with a degree of ambiguity typical of client environments, and sound judgment on when to escape versus resolve independently What we would like to see, but not required Hands‑on experience with Databricks, including Unity Catalog, Delta Lake, and MLflow Healthcare payer or provider claims data experience Experience with classical ML and with model fine‑tuning #J-18808-Ljbffr