ABOUT THE ROLE

Our Senior Media Platform Engineer role will build and operate the workflow orchestration and integration layer that powers FOX’s internal software-defined media services and AI-enabled workflows. This position focuses on developing the “glue” between platforms—workflow engines, microservices, APIs, data stores, and observability—so FOX can reliably deploy, scale, and support AI-assisted media capabilities across production and distribution environments.

This role works closely with multiple engineering teams and operational stakeholders to translate user needs into repeatable, governed workflows (POC → pilot → production) using modern software engineering practices and cloud-native delivery patterns. You will help FOX standardize how workflows are defined, validated, released, and monitored—especially for high-value AI use cases such as transcription, dubbing, highlights, video/audio analysis, and generative media services.

ABOUT YOU

• You are excited by new technology and its potential to change how things are done

• You LOVE getting stuff done, not just talking about it!

• You take ownership in your products and their success in the company

• You enjoy building systems that other teams depend on, and you care about reliability and maintainability

• You can translate ambiguous user needs into clear software requirements, APIs, and deliverable milestones

• You value automation, testing, and observability as first-class parts of engineering

• You like working across teams to evaluate options and deliver the best solution—not just “a” solution

A SNAPSHOT OF YOUR RESPONSIBILITIES

Workflow Orchestration & Platform Services

• Design and implement workflow orchestration for media and AI pipelines (job definitions, routing rules, scheduling constraints, approvals, retries, and error handling)

• Build and maintain platform services that standardize how workflows are executed and governed across environments (dev/test/prod)

• Develop reusable workflow templates, SDKs, and integration patterns that accelerate delivery and reduce one-off implementations

• Implement policy and guardrails for workload isolation (e.g., real-time vs batch vs experimental) in partnership with platform/SRE teams

AI Model Selection, Training & Lifecycle Enablement

• Partner with AI/ML stakeholders to evaluate model options (build vs buy vs fine-tune), including selection criteria such as quality, latency, cost, and operational complexity

• Support model implementation into production workflows, including packaging, versioning, deployment patterns, rollback strategies, and reproducibility

• Contribute to model lifecycle practices such as dataset/labeling requirements, evaluation/benchmarking metrics, A/B testing approaches, and periodic model refresh processes

• Help operationalize model governance: traceability of model versions, model performance monitoring in production, and release notes tied to workflow versions

Quality Gates, Validation & Operational Readiness

• Implement workflow-level validation and acceptance checks (input validation, output verification, automated quality gates) to ensure reliable, repeatable outcomes

• Define “production-ready” criteria for workflows (telemetry, error handling, documentation, runbooks, support handoffs) and enforce them through automation

• Partner with operations and reliability teams to ensure workflows can be supported in a 24/7 environment, with clear escalation paths and on-call expectations where appropriate

Software Development, CI/CD & Observability

• Build production-grade services with strong engineering discipline: code reviews, automated testing, secure coding practices, and documentation

• Implement and improve CI/CD pipelines for workflow services (build, test, deploy, promote, rollback) in partnership with platform teams

• Ensure services are observable and supportable (structured logs, metrics, tracing, dashboards, and operational runbooks)

• Perform root cause analysis and automate repetitive operational tasks to reduce toil and improve reliability

Collaboration & Client Outcomes (Internal + External)

• Work across engineering teams and stakeholders (Broadcast, Video, Monitoring & Control, Networking, Media Infrastructure) to define requirements, evaluate alternatives, and deliver the best solution for FOX workflows

• Translate user and operator requirements into clear user stories and acceptance criteria (“As a user, I need…”)

• Support internal clients (MCR/operations, production engineering, distribution teams) and, when applicable, improve FOX’s ability to support external partners through better reliability, visibility, automation, and repeatable delivery patterns

WHAT YOU WILL NEED

• 5+ years professional software engineering experience building and operating production services (APIs, microservices, distributed systems)

• Strong experience with cloud-native development practices: containers, service orchestration concepts, and CI/CD delivery

• Solid programming ability in one or more common backend languages (e.g., Python, Go, Java, TypeScript) and comfort building RESTful APIs and integrations

• Experience integrating with data systems (SQL/NoSQL), message/event systems, and modern authentication/authorization patterns

• Experience evaluating and integrating practical AI/ML capabilities into production systems, including automation, intelligent monitoring, workflow optimization, or developer tooling

• Strong troubleshooting mindset: ability to debug production issues and implement durable fixes

• Strong communication skills and comfort working cross-functionally with technical and non-technical stakeholders

NICE TO HAVE, BUT NOT A DEAL BREAKER

• Experience with workflow orchestration tools or event-driven architectures

• Familiarity with observability tooling and SRE-style operational practices (SLIs/SLOs, alerting, postmortems)

• Understanding of modern AI technologies such as LLM APIs, vector databases, and inference services, with the ability to apply them pragmatically in scalable, reliable environments

• Experience integrating AI/ML inference services, MLOps patterns, or contributing to model evaluation/fine-tuning workflows

• Familiarity with Kubernetes/OpenShift operations and infrastructure-as-code concepts

• Exposure to live media/broadcast concepts (helpful, but not required—we can teach)

• Bachelor’s Degree or equivalent practical experience in Engineering, IT, Computer Science or related technical field

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