Staff Platform Engineer

OVERVIEW OF THE COMPANY

Fox Corporation

Under the FOX banner, we produce and distribute content through some of the world’s leading and most valued brands, including: FOX News Media, FOX Sports, FOX Entertainment, FOX Television Stations and Tubi Media Group. We empower a diverse range of creators to imagine and develop culturally significant content, while building an organization that thrives on creative ideas, operational expertise and strategic thinking.

JOB DESCRIPTION

ABOUT THE ROLE

Build and mature the shared AI platform that MediaCloud teams use to deliver AI-enabled products and workflows, with a strong early focus on agentic workflow capabilities. This senior individual contributor role turns early AI patterns and pilot implementations into reusable, observable, governed platform capabilities that other engineering teams adopt.

A SNAPSHOT OF YOUR RESPONSIBILITIES

• Build shared agentic workflow components, orchestration primitives, and integration services used across MediaCloud teams.
• Design the platform for modularity so individual agents, models, frameworks, and tools can be swapped as AI tooling evolves.
• Implement platform standards for model access, prompt and workflow versioning, evaluation, observability, and reliability.
• Build on AWS AI and data services (Bedrock, SageMaker, Transcribe, Rekognition, S3, Lambda, ECS/EKS) as the preferred runtime surface.
• Partner with the Staff AI Solutions Engineer to decide what belongs in the shared platform and what stays team-owned.
• Support pilot and early-adopter teams by shipping reusable platform capabilities they can build on directly.
• Contribute to evaluation, telemetry, and support patterns so platform usage is measurable and supportable.
• Translate AI strategy into concrete technical implementation, with strong opinions on APIs, interfaces, and integration contracts.
• Use AI agentic workflows and code-generation tools as the primary way to produce code, and set the review bar for code that ships.
• Document platform capabilities so any engineer on the team can operate and extend them.
  

WHAT YOU WILL NEED

• Hands-on experience using AI agentic workflows and AI code-generation tools (for example, Claude Code, Cursor, or equivalent) as the primary way to produce code. Treats code review as a first-class discipline — reviews AI-generated and human-written code with equal rigor, spots subtle correctness, security, and maintainability issues, and holds a high bar on what ships.
• Strong software engineering background, ideally in platform engineering, developer tooling, or shared internal systems.
• Production experience on AWS, with working knowledge of AWS AI and data services (Bedrock, SageMaker, Transcribe, Rekognition, S3, Lambda, ECS/EKS).
• Experience designing and operating APIs, workflow orchestration, and service integrations at production scale.
• Working knowledge of AI-enabled applications, LLM-based systems, agentic workflows, or applied automation.
• Track record of designing systems for change — swappable components, clear interfaces, upgrade paths — so the platform absorbs new AI tooling as it emerges.
• Track record leading complex technical work that spans multiple teams.
• Strong judgment on reliability, observability, maintainability, and cost.
• Clear written communication. Comfortable defining technical direction in writing for other senior engineers.

NICE TO HAVE, BUT NOT A DEALBREAKER

• Experience in media, broadcast, or content-heavy environments.
• Experience shipping vector search and retrieval systems (Chroma, OpenSearch, pgvector, etc.).
• Experience building or operating agentic frameworks (tool-use, planning, multi-step orchestration).
• Experience integrating multiple model providers behind a common interface.

What Good Looks Like

• At least one reusable agentic workflow capability is in active production use on AWS, adopted by more than one team.
• Platform components are modular — the team swaps models, frameworks, or agent tools as better options appear, with minimal disruption to partner teams.
• Platform patterns are documented, adopted, and reduce duplicate implementation across teams.
• AI workflows running on the platform are observable, supportable, and meet reliability targets.
• Partner teams build on shared capability and extend it for their own use cases.
• AI-generated code that ships through this role meets the same quality bar as hand-written code.

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