AI Engineer

About The National Hockey League Founded in 1917, the National Hockey League (NHL®) is the premier professional ice hockey league in the world and is one of the major professional sports leagues in the United States and Canada.

With more than 1500 employees across the US and Canada, the NHL is a global sports and entertainment organization committed to building healthy and vibrant communities using the sport of hockey. At the NHL, we are looking for dynamic, energetic and impactful individuals who are committed to doing the same by sharing in our philosophy that Hockey is for Everyone.

Summary WHAT WE EXPECT OF YOU We are building the next generation of AI-powered capabilities across our data platform. As an AI Engineer, you will be a hands-on technical contributor who configures, orchestrates, and scales production-grade AI systems using platforms like Snowflake Cortex Agents, Claude. The role spans generative AI features, autonomous agentic workflows, and intelligent business process automation. You will work at the intersection of rapidly evolving AI platforms and real-world enterprise data, partnering closely with data engineers, analysts, product managers, and business stakeholders to deliver solutions that make a real difference.

Essential Duties And Responsibilities Agent orchestration and configuration

• Configure and deploy AI agents using managed platforms such as Snowflake Cortex Agents, Claude API, and extend them with custom tools and integrations where the platform falls short.
• Design multi-agent workflows including task handoffs, tool use, and human-in-the-loop escalation paths.

Business process automation

• Partner with stakeholders to identify where agents can replace or augment manual processes, then build integrations with internal systems including CRMs, ERPs, and data warehouses.
• Design fallback behaviors and human checkpoints for processes where fully autonomous action carries risk.

Data platform integration

• Connect AI agents to modern data platforms such as Snowflake or Databricks with appropriate access controls, and work within existing pipeline infrastructure rather than building parallel systems.

Evaluation, observability, and cost

• Define success criteria, build evaluation frameworks, and run structured tests before any system goes to production.
• Monitor agent behavior in production and manage inference cost versus output quality trade-offs across managed platforms.
• Monitor token utilization across agent workflows and advise teams on cost control and efficient platform usage.

Privacy, security, and responsible AI

• Apply GDPR, CCPA, and internal governance requirements across the full agent lifecycle, covering data access, logging, and outputs. Treat privacy-by-design as an architectural constraint from the start, not a review step at the end.
• Work with legal and compliance as a technical partner, and build fairness, explainability, and human oversight into agent workflows.

Communication and collaboration

• Translate AI capabilities and limitations clearly to non-technical stakeholders and contribute to internal guidelines so other teams can work with AI systems confidently and safely.

Qualifications Knowledge Areas/Experience

Required

• 5 or more years in software or data engineering, with at least 1 year working with LLM-based or agentic systems in production.
• Hands-on experience configuring and deploying agents on at least one managed platform such as Cortex Agents, Claude API, Bedrock, or Azure AI Foundry, with a track record of connecting AI to real business processes rather than demos.
• Python, REST APIs, MCP and event-driven architectures. Experience with prompt design, agent behavior configuration, and tool and function calling within managed platform frameworks.
• Proficiency with at least one cloud data platform such as Snowflake, or Databricks, and a solid understanding of data access patterns and governance sufficient to design agents that respect data boundaries.
• Ability to build lightweight CI/CD pipelines for deploying and updating agent configurations and working knowledge of what major AI platform providers offer, where their limits are, and when it makes sense to combine them.
• Experience with front end development and design tools like Figma; enough to shape how AI-powered interfaces look and feel, even if design is not your primary craft.
• Working knowledge of G…