Senior Backend Engineer
- Own backend services from content curation, to semantic search and content recommendation from design to production deployment
- Build and maintain microservices, cloud workflows and data pipelines that serve both internal editorial use-cases and consumer-facing applications
- Integrate with model serving endpoints, vector search APIs, and LLM inference services to power AI-driven features
- Build reliable, scalable cloud-native backend infra on multi-cloud platforms (GCP, AWS, Azure) with consideration for availability, performance, and cost
- Mentor junior engineers, conduct code and design reviews, and take ownership of code quality, test coverage, and documentation
- Collaborate with ML engineers, data engineers, and product leaders to craft end-to-end solutions
- Ensure all systems adhere to security best practices for compliance with Fox policies on data protection and user privacy
- Follow TDD/BDD approach with automated testing (unit, integration, end-to-end) practices to ensure quality
- Proven track record of backend engineering experience working on production-grade systems in a product-focused organization.
- Strong programming skills in TypeScript and/or Python with solid computer science foundation in data structures and algorithms
- Experience designing and building RESTful APIs and microservices at scale
- Expertise in distributed systems design and architecture with focus on reliability and scaling
- Experience with various types of data processing and persisting solutions (Apache Spark, PostgreSQL, MongoDB, Redis, Kafka)
- Cloud platform experience (GCP preferred: Cloud Run, Cloud Functions, Pub/Sub, Cloud Storage, Workflow, Firestore, AlloyDB, BigQuery)
- Experience integrating LLMs and other ML models serving endpoints
- Understanding of multi-modal embeddings, vector search, and various similarity/semantic search patterns
- Strong analytical and problem-solving skills with ability to understand complex systems
- Experience with CI/CD pipelines, containerization (Docker), and infrastructure-as-code
- Ownership mindset with end-to-end responsibility for design, implementation, and production operation
- Excellent grasp of SDLC and agility methodology, well versed with challenges in developing and releasing a globally distributed team
- Experience with AI platforms and ML model serving infrastructure (e.g., Vertex AI, SageMaker, or similar)
- Experience with media processing pipelines and delivery systems (e.g. new articles, episodes, blogpost, video, podcasts)
- Familiarity with vector databases and semantic search systems (e.g. pgVector, Opensearch/Elasticsearch, Pinecone)
- Experience with Google Cloud Vertex AI and other AI service (e.g. OpenAI, Anthropic Claude, ElevenLabs)
- Curiosity and enthusiasm for generative AI, LLMs, and Agentic workflows
- Open-source contributions or active participation in the developer community
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