National Hockey League (NHL)
AI Engineer
National Hockey League (NHL) - Director
New York · NY · Hybrid
Technology · Technology Director · Technical/Engineering
$200,000 - $225,000 / year
0
7
hours ago
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.
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.
WHAT WE EXPECT OF YOU
SUMMARY
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 GDPR, CCPA, and internal data governance requirements, with demonstrated ability to apply privacy-by-design principles in system architecture and to engage legal and compliance teams as a technical partner.
Preferred
- Background in robotic process automation or business process management
- Multimodal agent workflows
- Open-source contributions in the AI and ML space
Education/Certifications
- A degree in Computer Science, Data Science, or a related field is preferred
- Relevant cloud or AI certifications such as AWS ML Specialty, Azure AI Engineer, or Snowflake SnowPro are a plus, though demonstrated hands-on experience carries more weight than credentials alone.
Skills
- Demonstrates strong judgment in determining when to leverage managed AI platforms and when custom engineering solutions are more appropriate.
- Works independently, defines practical approaches to ambiguous problems, and advances initiatives with a high degree of ownership.
- Approaches privacy, security, and governance as core engineering responsibilities throughout the solution lifecycle.
- Consistently considers fallback design, monitoring, and human oversight before production deployment, and communicates system behavior clearly to technical and non-technical audiences.
- Brings a strong user-centered mindset, with careful attention to the usability, clarity, and overall experience of the solutions built.
- Maintains current knowledge of the evolving AI platform landscape and applies emerging capabilities thoughtfully to improve solution design and delivery.
- Strong Microsoft Office skills, particularly Excel and PowerPoint.
- Highly developed verbal and written communication skills with the ability to influence at leadership levels.
- Demonstrable proficiency in data analysis and assessment abilities.
- Highly organized, attention to details and strong follow-through.
- A positive energetic attitude.
CORE COMPETENCIES
These core competencies reflect the underlying values that are necessary to represent the National Hockey League:
- Accountability
- Adaptability
- Communication
- Critical Thinking
- Inclusion
- Professionalism
- Teamwork & Collaboration
The NHL offers U.S. regular, full-time employees:
Time to Recharge: Utilize our generous Paid Time Off (PTO) to focus on your well-being and ensure a healthy work/life balance. PTO includes paid holidays, vacation, personal and sick days, plus an extra day off for your birthday.
Ability to Focus on your Health: Along with competitive salaries, the NHL offers comprehensive health benefits to employees and their eligible dependents effective on their first day with us – there is no waiting period. The NHL subsidizes a large portion of the health benefits costs, therefore your cost for medical, dental and vision coverage is minimal.
We also offer our employees and members of their household access to our Employee Assistance Program (EAP) to support mental, physical, and financial health. In addition, employees have access to a digital wellness resource designed to improve health and happiness through courses in sleep, movement, and focus. These services are confidential and at no-cost to our employees.
Childcare Leave: Because your family is the NHL family, employees are offered comprehensive Childcare Leave to welcome your new addition. The primary caregiver to the child is entitled to up to 12 weeks of paid Childcare Leave, at full pay, following the birth, adoption, or placement of a child.
Employees that are not the primary caregiver to the child are entitled to up to 6 weeks of paid Childcare Leave, at full pay, which must be taken within the first 6 months following the birth, adoption, or placement of a child.
Confidence in your Retirement Goals: Participate in the NHL’s Savings Plan which includes a 401K (pre-tax and Roth options) plus non-elective (employer) contributions to keep your retirement goals on track.
A Hybrid Work Schedule: The NHL recognizes the value of flexibility in work locations/schedules to help our employees balance work/life priorities. Hybrid work schedules are available for a majority of our roles.
Our New Headquarters: Our new, state of the art, offices are located at One Manhattan West in Hudson Yards. When you’re in the office, you can conduct meetings in one of our high-tech conference rooms, have lunch with a view or play in the game room. Employees can also enjoy New York’s newest neighborhood that is home to more than 100 shops, culinary experiences, and public artwork.
A Savings for Commuting: Participate in the NHL’s pre-tax commuter benefit plan which helps offset the financial cost of traveling to and from our office.
NHL Partner Rates: Unlock exclusive pricing from our Partners that include savings on travel, consumer goods and services, plus the NHL Store.
Life at the NHL: In your first few days, you meet with your new teammates and the HR Team. You have the opportunity to learn more about the NHL and our workplace culture. Employees are invited to play hockey during our Tuesday Night Skate at Chelsea Piers, join our Employee Resource Groups and more. You are a part of our team and we encourage you to be your authentic self, adding to our dynamic workplace culture.
SALARY RANGE:
$200-225K
Actual base pay for a successful candidate will be determined based on a variety of job-related factors, including but not limited to: experience/training, market demands, and geographic location.
When applying, please be sure to include a cover letter with your salary expectations for this role. We thank all applicants for their interest in this opportunity, however only qualified candidates selected for an interview will be contacted. NO EMAILS OR PHONE CALLS PLEASE.
We are an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, sex, sexual orientation, age, disability, gender identity, marital or veteran status, or any other protected class.
Job Questions:
Are you looking to work hybrid in our New York City office or fully remote?
If you are looking for a hybrid position, are you willing and able to commute to our New York City office?
If you are looking for a hybrid position and not currently living in the tri-state area, are you willing and able to relocate at your own expense for this position?
Do you have the legal right to work in the United States?
Will you now or in the future require visa sponsorship to continue work in the United States?
What are your salary expectations for this role? (NOTE: We are NOT asking for your current salary or salary history)
How did you hear about this position? Where did you first see this role posted?
Give us an example of a time you configured and deployed an AI agent on a managed platform such as Snowflake Cortex Agents, Claude API, Bedrock, or Azure AI Foundry. What did you build, what challenges did you run into, and how did you handle them?
Describe a situation where an AI agent or LLM-based system you built did not behave as expected in production. How did you detect it, what did you do, and what did you change going forward?
On a scale of 1 to 5, how would you rate your experience with prompt design and agent behavior configuration? Walk us through a specific example that reflects that rating. How have you approached data privacy and security when building AI systems? Give us a concrete example of a decision you made that put privacy-by-design into practice.
Tell us about a time you had to explain an AI system or its limitations to a non-technical stakeholder. How did you approach it and what was the outcome?
How do you currently stay current with the AI platform landscape? What recent development has changed how you think about building agent-based systems?
