Engineering Manager, Search & Consumer AI

New

Location: New York, NY, United States Workplace Type: RemoteFull-TimeManager

Salary$190,000 — $237,500 USD

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Job Details

Experience: 1-3+ years managing a software engineering team, or significant tech-lead experience with people-leadership responsibilities
Required Skills: AWSJavaKafkaSalesforceNLP

Have 1-3+ years managing a software engineering team, or significant tech-lead experience with people-leadership responsibilities.
Have experience delivering complex, high-scale systems.
Have experience in a Java and AWS ecosystem.
Have experience shipping consumer-facing AI/ML or NLP features to production.
Have experience integrating LLMs and building pipelines and evaluation practices to keep them reliable.
Have a strong understanding of search, discovery, or relevance systems, including traditional search and LLM-based approaches.
Demonstrate strong technical judgment and clear communication with engineers, Product partners, and leadership.
Have the ability to mentor engineers and build a healthy, high-performing team.
Be open to occasional travel to Fanatics offices and other locations for conferences, events, meetings, and team building.
AWS Bedrock or equivalent managed AI services experience is preferred.
Experience with customer support AI or embedded consumer AI experiences is preferred.
Familiarity with Kafka, Airflow, dbt, OpenSearch, Kotlin, and Salesforce is preferred.
Experience building shared platforms consumed by multiple product teams is preferred.

Lead end-to-end delivery across Search and Consumer AI, including scope, sequencing, blocker removal, and risk management.
Set reliability, performance, and relevance quality standards, with observability for AI and search services.
Ensure the team's work meets data governance, security, compliance, and responsible AI standards.
Own on-call, incident response, and operational health for the team's systems.
Partner with Product and engineering leadership to shape planning and roadmaps.
Work with product teams across the ecosystem to embed AI experiences and agree on shared ownership, interfaces, and quality bars.
Guide how LLMs and ML models are evaluated, integrated, and rolled out in production.
Hire, onboard, mentor, and develop engineers, providing timely feedback and supporting career growth.
Build team processes for planning, reviews, retrospectives, and experimentation.

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