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SponsorUnited

Senior Software Engineer-(AI/GenAI Solutions Architect)

Posted 23 Days Ago
In-Office or Remote
8 Locations
Senior level
In-Office or Remote
8 Locations
Senior level
The Senior Software Engineer will architect and lead AI-driven solutions, focusing on automation, data extraction, and mentoring engineers while integrating diverse technologies and maintaining system reliability.
The summary above was generated by AI

SponsorUnited is one of the fastest growing sports & entertainment technology platforms in the world, connecting the partnership ecosystem by providing the most comprehensive and relevant marketing and sales data available anywhere. 

Almost every major pro sports team uses our platform, and our customers currently span 1,200+ organizations across sports, music, media, brands and agencies.

Job Description:

SponsorUnited is seeking a Senior Software Engineer-(AI/GenAI Solutions Architect) to drive innovation in AI-powered automation and data extraction across our platform. This role goes beyond implementation—you will architect, prototype, and lead the development of cutting-edge AI/GenAI solutions, with a focus on agentic workflows, automated data pipelines, and rapid experimentation. You will work cross-functionally to transform concepts into production-ready tools and processes that scale, while staying current on evolving AI/ML trends and technologies.

As a technical leader, you will help shape SponsorUnited’s AI roadmap, mentor engineers, and champion emerging technologies across the organization.

Key Responsibilities:

  1. AI/GenAI Architecture & Prototyping
    1. Architect and prototype AI-driven systems focused on automated data extraction, agentic workflows, and LLM orchestration.
    2. Rapidly experiment with frameworks like LangChain, LangGraph, and LangSmith, and llms like OpenAI, Anthropic, and Gemini api solutions to validate ideas and accelerate deployment.
    3. Design and direct the implementation of retrieval-augmented generation (RAG) pipelines, chunking strategies, and autonomous agent systems.
  2. AI Integration:
    1. Integrate and deploy AI solutions, including RAG (Retrieval-Augmented Generation) and chunking techniques for large language models.
    2. Leverage frameworks such as LangChain, LangGraph, LangSmith, or similar modern high-level AI libraries.
  3. Database and Storage:
    1. Implement and optimize relational (PostgreSQL), NoSQL (DynamoDB), and graph databases for diverse data needs.
    2. Utilize vector stores (Chroma, Zillis, etc.) to support AI-driven features.
    3. Use Redis for caching and real-time data processing.
    4. Tune queries and database structures for high-volume data ingestion and retrieval.
  4. Microservices & Scalability:
    1. Design and deploy microservices architectures using Docker and Kubernetes.
    2. Ensure services are highly available, scalable, and fault-tolerant.
    3. Collaborate with DevOps to optimize CI/CD pipelines (Jenkins, GitHub Actions, CircleCI).
  5. Security and Authorization:
    1. Implement OAuth 2.0 and other secure mechanisms for user authentication and authorization.
    2. Conduct regular security assessments, address vulnerabilities proactively, and adhere to industry best practices.
  6. Testing & Quality Assurance:
    1. Drive testing best practices, including unit tests, integration tests, end-to-end (e2e) tests, and evaluation for AI components.
    2. Collaborate with QA to ensure coverage across all features and regression scenarios.
  7. Performance, Reliability & Monitoring:
    1. Use metrics, monitoring, and alerting systems to track performance, usage, and system health.
    2. Troubleshoot, diagnose, and resolve production issues with minimal downtime.
    3. Continuously improve logging and observability to enhance reliability.
  8. Collaboration & Leadership:
    1. Work closely with Product, Design, and DevOps to translate business requirements into robust technical solutions.
    2. Participate in code reviews, architectural discussions, and strategic technical decision-making.

Qualifications:

Required:

  • 8+ years of professional experience in software engineering with a full stack focus.
  • Deep expertise in cloud platforms (AWS, GCP, or Azure).
  • Proficient with containerization (Docker) and orchestration (Kubernetes) for microservices.
  • Hands-on experience with CI/CD tools (Jenkins, GitHub Actions, CircleCI) and GitHub workflows.
  • Strong proficiency in Python (FastAPI) and TypeScript (Angular).
  • Solid understanding of webhooks, websockets, and real-time communication patterns.
  • Extensive experience with relational (PostgreSQL) and NoSQL (DynamoDB) databases; familiarity with graph databases is a plus.
  • Experience with vector databases (Chroma, Pinecone, etc.) for AI/ML use cases.
  • Proven track record implementing OAuth 2.0 or similar authentication/authorization frameworks.
  • Demonstrated ability to deliver secure, high-performing, and scalable web applications.
  • Experience working with state-of-the-art AI/ML frameworks. LangChain, LangGraph, or LangSmith is a plus.
  • Advanced understanding of code quality, testing (unit, e2e, evaluation), and modern best practices in software development.
  • Excellent communication skills, collaborative mindset, and strong problem-solving abilities.

Preferred:

  • Experience building complex front-end applications with NX or other monorepo tooling.
  • Knowledge of data pipelines, ETL workflows, and event-driven systems.
  • Interest in sports, entertainment, and media industries is a plus.

Top Skills

AWS
Azure
Chroma
CircleCI
Docker
DynamoDB
GCP
Github Actions
Jenkins
Kubernetes
Langchain
Langgraph
Langsmith
Oauth 2.0
Postgres
Python
Redis
Typescript

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