Senior Data & AI Backend Engineer (Hybrid)

PlayOn Sports

United States

Hybrid

USD 140,000 - 190,000

Full time

7 days ago
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Benefits offered by this job

Medical insurance
Dental, vision, life and disability
Employee equity
Open PTO
401K with company match
Hybrid/flexible work environment

Job summary

PlayOn Sports is seeking a Senior Software Engineer to build AI-powered, data-driven services for web experiences used by millions of high school sports fans. You’ll own backend delivery, work on data schemas, APIs, and LLM integrations, and partner with product and engineering to shape how sports data is modeled and exposed.

You’ll develop modern backend services with C# .NET, Python, SQL, AWS, and Snowflake, drive deployment and observability, and ensure production AI features meet quality

Qualifications

  • 5+ years of professional software engineering experience.
  • Experience building production features backed by LLMs.
  • Hands-on experience with cloud data warehouses (Snowflake).
  • Strong expertise in C# .NET, Python, SQL, REST APIs, and data modeling.
  • Experience with CI/CD pipelines and Git workflows.

Responsibilities

  • Develop backend services and APIs.
  • Build LLM-powered features using retrieval and semantic search.
  • Plan migrations and modernization with low risk.
  • Ensure observability and performance of services.
  • Own end-to-end feature delivery.

Skills

C# .NET
Python
SQL
REST API design
LLM integration
AWS
PostgreSQL
Data modeling
CI/CD
Team collaboration

Education

Bachelor's in Computer Science or equivalent

Tools

Snowflake
Git/GitHub
PostgreSQL

Job description

PlayOn Sports is seeking a Senior Software Engineer to build AI-powered, data-driven services for web experiences used by millions of high school sports fans. You’ll own backend delivery, work on data schemas, APIs, and LLM integrations, and partner with product and engineering to shape how sports data is modeled and exposed.

You’ll develop modern backend services with C# .NET, Python, SQL, AWS, and Snowflake, drive deployment and observability, and ensure production AI features meet quality

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