AI/ML Engineer

Summary

Tripleseat is a company with a Data Engineering team and AI Products team focused on data-driven capabilities within its core products. The AI/ML Engineer will build a semantic layer over Snowflake and dbt, enable natural-language AI interaction with data, and develop reliable AI-powered analytics and customer-facing intelligence features.

Responsibilities

• Design and build a semantic layer on top of Snowflake and dbt that defines metrics, dimensions, and business logic consistently across the company
• Partner with data engineers to align the semantic layer with upstream dbt models and pipeline changes
• Work with business and product stakeholders to capture how they define and use key metrics, translating that into the semantic layer
• Evaluate and implement semantic layer/metrics-layer tooling (e.g., Hex, Cube, dbt Semantic Layer, Snowflake MCP server) that best fits Tripleseat's stack
• Build tools and integrations that let business and product teams query and analyze Snowflake data using natural language and AI agents
• Design guardrails, evaluation, and grounding strategies so AI-generated answers over Snowflake data are accurate and trustworthy
• Create reusable prompts, tools, and context (via the semantic layer) that make AI-assisted analytics reliable and self-serve
• Train and support business and product teams in using these AI tools effectively
• Partner with our AI Products team to help design and build AI/ML-powered features embedded in Tripleseat's core products, drawing on Snowflake data and the semantic layer
• Partner with Product and Engineering to identify opportunities where data and AI can create customer-facing value
• Own the path from Snowflake data to production-grade intelligence features, including latency, cost, and reliability considerations
• Set best practices for AI/ML tooling and semantic layer design across the data platform
• Stay current on emerging AI tooling for data interaction and evaluate what's worth adopting at Tripleseat
• Mentor team members on AI-assisted analytics and semantic layer concepts

Skills

• 3+ years in an AI/ML engineering, analytics engineering, or data engineering role, with hands-on AI/ML project experience
• Strong SQL skills and experience working with a cloud data warehouse, ideally Snowflake
• Experience building semantic layers, metrics layers, or similar abstractions over data models (e.g., dbt Semantic Layer, Cube, LookML)
• Proficiency in Python, including experience building with LLM APIs, agent frameworks, or RAG pipelines
• Experience with dbt or similar transformation tools, and comfort reading/extending dbt models
• Experience designing AI features that ship to production, including evaluation and guardrails for accuracy
• Comfortable working with Git for version control and collaborative development
• Strong communicator who can translate business and product needs into technical data and AI solutions
• Direct experience with the Snowflake MCP server or other Snowflake AI tooling (Cortex, Snowflake Copilot)
• Experience shipping customer-facing AI or ML features inside a SaaS product
• Familiarity with vector databases and retrieval-augmented generation architectures
• Experience with pipeline orchestration tools (e.g., Dagster, Airflow)
• Familiarity with AWS or another cloud environment
• Experience leveraging AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude) to accelerate workflows
• Background working in a fast-growing SaaS company or startup environment

Qualifications

Must Haves

• 3+ years in an AI/ML engineering, analytics engineering, or data engineering role, with hands-on AI/ML project experience
• Strong SQL skills and experience working with a cloud data warehouse, ideally Snowflake
• Experience building semantic layers, metrics layers, or similar abstractions over data models (e.g., dbt Semantic Layer, Cube, LookML)
• Proficiency in Python, including experience building with LLM APIs, agent frameworks, or RAG pipelines
• Experience with dbt or similar transformation tools, and comfort reading/extending dbt models
• Experience designing AI features that ship to production, including evaluation and guardrails for accuracy
• Comfortable working with Git for version control and collaborative development
• Strong communicator who can translate business and product needs into technical data and AI solutions
• Direct experience with the Snowflake MCP server or other Snowflake AI tooling (Cortex, Snowflake Copilot)
• Experience shipping customer-facing AI or ML features inside a SaaS product
• Familiarity with vector databases and retrieval-augmented generation architectures
• Experience with pipeline orchestration tools (e.g., Dagster, Airflow)
• Familiarity with AWS or another cloud environment
• Experience leveraging AI-assisted development tools (e.g., GitHub Copilot, Cursor, Claude) to accelerate workflows
• Background working in a fast-growing SaaS company or startup environment

Benefits

• Competitive Medical, Dental, and Vision Insurance: Comprehensive coverage to support health and well-being.
• Company Paid Life Insurance, Short- and Long-Term Disability Plans: Protection and peace of mind in unforeseen circumstances.
• Supplemental Insurance Options: Optional critical illness, accident, and hospital plans.
• Commuter Benefits: Transit and parking benefits, where applicable.
• 401(k) with Company Match: Helping employees save and plan for retirement.
• Parental Leave: Support for new parents through birth, adoption, or foster care.
• Flexible Paid Time Off: Encouraging work-life harmony and balance.
• LinkedIn Learning Access: On-demand professional development courses.
• Pet Insurance: Supporting the well-being of furry family members.
• Employee Discount Programs: Exclusive savings through employee perk platforms.