Staff Software Engineer AIML Platform

Fanatics Commerce is the global leader in licensed sports merchandise operating a vertically integrated platform that designs manufactures and delivers officially licensed apparel jerseys headwear and collectibles for major leagues teams and events worldwide. With more than 900 e-commerce sites and a global omnichannel presence across digital in-venue and retail Fanatics Commerce reaches fans in over 180 countries and powers official fan experiences for many of the worlds most iconic sports properties.

At Fanatics we bring our BOLD Leadership Principles to life every day - building championship teams obsessing over fans acting with entrepreneurial speed and delivering with a determined and relentless mindset.

About the team
The AI/ML Platform pod builds Fanatics internal AI platform: the paved road for every teambuilding with LLMs MCPs and agents. Gateway agent runtime registry retrieval evaluation
and cost analytics all running on a petabyte-scale lakehouse with enterprise-grade governance. Youll join a pod that owns this platform end to end. This is a Staff-level role and a hands-on
one. You will choose the architecture define the standards other teams build to and stay in thecode shipping the hardest parts yourself. The decisions you make are ones the organization
lives with for years.

Responsibilities
Own the end-to-end architecture of the AI control plane: gateway agent runtime registry retrieval evaluation and cost. Set technical direction sequence the roadmap and make the build-versus-buy calls.
Build the enterprise LLM gateway: multi-provider routing failover caching rate limiting token optimization and key management with a path to self-hosted open-weight models where cost latency or data residency call for it.
Build the Agent/MCP ecosystem on a managed runtime such as AWS BedrockAgentCore: enterprise systems exposed as governed tools over MCP a control-planeregistry for discovery ownership versioning and lifecycle and a no-code path fromprototype to governed production agent surfaced through a company-wide chat portal.
Own the unified retrieval layer and the data platform behind it: the governed front door plus the ingestion chunking embedding and indexing pipelines and the vector andsearch backends that feed it kept reproducible observable and quality-gated atpetabyte scale.
Build the evaluation and feedback loop: offline evals tracing and regression gates inCI/CD with production failures and user edits turning into new eval cases and root-cause attribution across prompts retrieval tools and models.
Own governance and cost: RBAC agent identity least-privilege access to enterprise data audit logging use-case-level cost attribution and the enterprise AI tool portfolio standing up to finance executive and EU AI Act scrutiny.
Raise the bar: set the engineering standards mentor engineers and represent thearchitecture and its trade-offs directly to senior leadership

Qualifications
8 years building production software including 2 years on AI/ML platform. Bachelors or Masters in Computer Science Engineering or a related field or equivalent experience.
Staff-level impact: system design across multiple services decisions that span teamsand platforms other engineers build on.
Strong Python and FastAPI plus one of Java GoLang Scala or TypeScript. Hands-onwith Terraform Kubernetes Airflow Postgres Grafana and Prometheus and familiar
enough with the AWS data science toolkit and libraries like PyTorch to partner crediblywith data scientists. Chat UI or internal developer portal work is welcome.
Hands-on with LLMs and generative AI on Bedrock or Vertex: model routing RAG toolcalling structured outputs and context engineering. Agentic systems and MCP serverand client integrations. Agent runtimes such as AWS Bedrock AgentCore matter hereand because they are new we look for depth rather than years. Exposure to self-hostedserving with vLLM SGLang or is welcome.
The data engineering behind retrieval: chunking embedding and indexing pipelinesvector or search backends such as OpenSearch open lakehouse table formats and aworking method for measuring retrieval quality.
LLM evaluation and observability: eval dataset design tracing LLM-as-judge scoringand regression testing with tools such as Langfuse or Arize Phoenix.
Security and governance for AI platforms: RBAC OAuth2/OIDC least privilege costoptimization and usage metering. Familiarity with governance systems like DataHuband semantic layers and with administering enterprise AI tools such as ChatGPTEnterprise Claude Enterprise or Glean is welcome.
Writes clearly and holds the room with executives. Open-source contributions orpublished work in agentic AI or LLMOps is welcome

Required Experience:

Staff IC