Generative AI Engineer

Your day to day

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

Client-Facing / Forward Deployed Engineering

• 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 escalate 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
• Relevant certifications (e.g. SnowPro)