Senior Backend Engineer - Generative AI / Personalization

Note: This is for a LOA coverage.

This is a remote role but you must work EST hours.

Our media client owns and innovates on the AI DJ interactive listening experience, powering high-volume personalized audio features for over 20 million weekly active users worldwide. As they prepare to scale the AI DJ across new languages and new international markets, we are looking for a Senior Backend Engineer to join our team. In this role, you will help own our backend infrastructure, improve DJ storytelling capabilities, and build agentic workflows that integrate existing backend microservices with cutting-edge Large Language Models (LLMs).

What You'll Do

• High-Volume Backend Engineering: Design, build, scale, and maintain high-throughput, low-latency Java backend services powering the interactive AI DJ experience.
• Agentic Workflows & LLM Integration: Develop agentic workflows, skill integrations, and tools around established LLM systems (e.g., Anthropic, Google, Cohere) that connect internal microservices and databases to dynamic AI features.
• Global Scale & Reliability: Support the expansion of backend systems as the platform scales to new languages and new markets.
• Cross-Functional Architecture: Lead cross-stack architectural decisions and collaborate closely with client side, mobile, C++ player engineers, Product Managers, and Data Scientists.
• System Testing & Experimentation: Run backend experiments replacing traditional services with LLM-generated content while ensuring high performance, stability, and fast iteration cycles.

Who You Are

• Backend Expertise: You have 6+ years of engineering experience with deep technical proficiency in core Java and building distributed, high-volume backend microservices.
• High-Scale/High-Velocity Background: Experience maintaining production systems handling millions of users in high-volume, fast-paced environments (e.g., high-throughput tech platforms, large consumer internet products, or high-frequency systems).
• Applied AI / LLM Experience: Hands-on experience building agentic workflows, tool/API integrations, or RAG systems on top of existing LLM models (Note: Fine-tuning or building models from scratch is not required).
• Cross-Team Leadership: Proven ability to drive architectural consensus across different engineering teams (mobile, player engines, AI/ML).