Staff Software Engineer - AI/ML Platform

Fanatics; San Francisco (CA); Sur place; USD 190 000 - 280 000; Plein temps

Résumé du poste

Fanatics Commerce in Redwood City, CA, is seeking a Staff Software Engineer for its AI/ML Platform team. You will own the architecture of the AI control plane, including gateway, runtime, registry, retrieval, and evaluation components, and drive cost governance across the platform.

The role demands hands-on work with enterprise-grade tooling, multi-provider LLM routing, and scalable data pipelines, partnering with data scientists and engineers to deliver a robust, self-hosted path when needed.

Qualifications

• 8+ years building production software, including 2+ years on AI/ML platform.
• Staff-level impact: design across services and platforms used by other engineers.
• Strong Python and FastAPI, plus one of Java/Go/Scala/TypeScript; hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, Prometheus; familiar with PyTorch.

Responsabilités

• Own the end-to-end architecture of the AI control plane: gateway, agent runtime, registry, retrieval, evaluation, and cost.
• Build the enterprise LLM gateway: multi-provider routing, failover, caching, rate limiting, and data residency considerations.
• Build the Agent/MCP ecosystem on a managed runtime (e.g., AWS BedrockAgentCore) with governed tools and no-code paths to production.
• Own the unified retrieval layer and data platform: ingestion, embedding, indexing, and vector/search backends at scale.
• Develop the evaluation and feedback loop: offline evals, tracing, regression gates in CI/CD, and root-cause attribution across prompts, retrieval, tools, and models.
• Own governance and cost: RBAC, audit logging, and enterprise AI tool portfolio oversight; interface with finance and legal teams.
• Raise the bar: set engineering standards, mentor engineers, and present trade-offs to senior leadership.

Connaissances

Python

FastAPI

Java

Go

TypeScript

Terraform

Kubernetes

Airflow

Postgres

Grafana

Prometheus

PyTorch

Formation

Bachelor's or Master's in CS/Engineering

Outils

Bedrock

Vertex

AWS Bedrock AgentCore

Description du poste

Staff Software Engineer - AI/ML Platform

Redwood City, CA, United States

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 world's 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. You'll 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 the code, 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 from prototype 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 and search backends that feed it, kept reproducible, observable, and quality-gated at petabyte scale.
• Build the evaluation and feedback loop: offline evals, tracing, and regression gates in CI/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 the architecture and its trade-offs directly to senior leadership.

Qualifications

• 8+ years building production software, including 2+ years on AI/ML platform. Bachelor's or Master's in Computer Science, Engineering, or a related field or equivalent experience.
• Staff-level impact: system design across multiple services, decisions that span teams, and platforms other engineers build on.
• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar enough with the AWS data science toolkit and libraries like PyTorch to partner credibly with 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, tool calling, structured outputs, and context engineering. Agentic systems and MCP server & client integrations. Agent runtimes such as AWS Bedrock AgentCore matter here, and because they are new we look for depth rather than years. Exposure to self-hosted serving with vLLM, SGLang, or llama.cpp is welcome.
• The data engineering behind retrieval: chunking, embedding, and indexing pipelines, vector or search backends such as Open