Asks for

The ad says“• 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 and 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 ad says“• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar”
The ad says“• LLM evaluation and observability: eval dataset design, tracing, LLM-as-judge scoring, and regression testing, with tools such as Langfuse or Arize Phoenix.”
The ad says“• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar”
The ad says“• 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 and 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 ad says“• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar”
The ad says“• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar”
The ad says“• LLM evaluation and observability: eval dataset design, tracing, LLM-as-judge scoring, and regression testing, with tools such as Langfuse or Arize Phoenix.”
The ad says“• 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 and 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 ad says“• Security and governance for AI platforms: RBAC, OAuth2/OIDC, least privilege, cost optimization, and usage metering. Familiarity with governance systems like DataHub and semantic layers, and with administering enterprise AI tools such as ChatGPT Enterprise, Claude Enterprise, or Glean, is welcome.”
The ad says“• The data engineering behind retrieval: chunking, embedding, and indexing pipelines, vector or search backends such as OpenSearch, open lakehouse table formats, and a working method for measuring retrieval quality.”
The ad says“• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar”
The ad says“• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar”
The ad says“• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar”
The ad says“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.”
The ad says“• 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 and 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 ad says“• Security and governance for AI platforms: RBAC, OAuth2/OIDC, least privilege, cost optimization, and usage metering. Familiarity with governance systems like DataHub and semantic layers, and with administering enterprise AI tools such as ChatGPT Enterprise, Claude Enterprise, or Glean, is welcome.”
The ad says“• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar”

Values

The ad says“• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar”
The ad says“• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar”
The ad says“• 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 and 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 ad says“• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar”
The ad says“• 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 and 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 ad says“• Strong Python and FastAPI, plus one of Java, GoLang, Scala, or TypeScript. Hands-on with Terraform, Kubernetes, Airflow, Postgres, Grafana, and Prometheus, and familiar”
The ad says“• 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 and 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.”

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 team building 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-plane registry 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 and 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 OpenSearch, open lakehouse table formats, and a working method for measuring retrieval quality.
• LLM evaluation and observability: eval dataset design, tracing, LLM-as-judge scoring, and regression testing, with tools such as Langfuse or Arize Phoenix.
• Security and governance for AI platforms: RBAC, OAuth2/OIDC, least privilege, cost optimization, and usage metering. Familiarity with governance systems like DataHub and semantic layers, and with administering enterprise AI tools such as ChatGPT Enterprise, Claude Enterprise, or Glean, is welcome.
• Writes clearly and holds the room with executives. Open-source contributions or published work in agentic AI or LLMOps is welcome

The salary range represents base pay only and does not include short-term or long-term incentive compensation. When determining base pay as part of a final compensation package, we consider several factors such as location, experience, qualifications, and training. For information about our benefits, please visit https://benefitsatfanatics.com/

Salary Range

$190,000—$237,500 USD

At Fanatics, we operate with a BOLD mindset - We Build Championship Teams, we’re Obsessed with Fans, we embrace a Limitless Entrepreneurial Spirit, and we approach every challenge with a Determined and Relentless Mindset. If you’re ready to contribute to a dynamic, fast-paced environment that thrives on collaboration and growth, we want you to be part of our team.

ABOUT US
Fanatics is building a leading global digital sports platform. We ignite the passions of global sports fans and maximize the presence and reach for our hundreds of sports partners globally by offering products and services across Fanatics Commerce, Fanatics Collectibles, and Fanatics Betting & Gaming, allowing sports fans to Buy, Collect, and Bet. Through the Fanatics platform, sports fans can buy licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods; collect physical and digital trading cards, sports memorabilia, and other digital assets; and bet as the company builds its Sportsbook and iGaming platform. Fanatics has an established database of over 100 million global sports fans; a global partner network with approximately 900 sports properties, including major national and international professional sports leagues, players associations, teams, colleges, college conferences and retail partners, 2,500 athletes and celebrities, and 200 exclusive athletes; and over 2,000 retail locations, including its Lids retail stores. Our more than 22,000 employees are committed to relentlessly enhancing the fan experience and delighting sports fans globally.

ABOUT THE TEAM
Fanatics Commerce is a leading designer, manufacturer, and seller of licensed fan gear, jerseys, lifestyle and streetwear products, headwear, and hardgoods. It operates a vertically-integrated platform of digital and physical capabilities for leading sports leagues, teams, colleges, and associations globally – as well as its flagship site, www.fanatics.com.

Fanatics Commerce has a broad range of online, sports venue, and vertical apparel partnerships worldwide, including comprehensive partnerships with leading leagues, teams, colleges, and sports organizations across the world—including the NFL, NBA, MLB, NHL, MLS, Formula 1, and Australian Football League (AFL); the Dallas Cowboys, Golden State Warriors, Paris Saint-Germain, Chelsea FC, and Tokyo Giants; the University of Notre Dame, University of Alabama, and University of Texas; the International Olympic Committee (IOC), England Rugby, and the Union of European Football Associations (UEFA).

At Fanatics Commerce, we infuse our BOLD Leadership Principles in everything we do:
· Build Championship Teams
· Obsessed with Fans
· Limitless Entrepreneurial Spirit
· Determined and Relentless Mindset

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