Backend Engineer, AI

We’re looking for a backend-focused engineer to help build and scale the infrastructure behind AI-driven products. The role involves working across application services, model integration, and distributed systems, with a strong emphasis on building reliable production software. You’ll be responsible for turning AI capabilities into dependable backend services that can support real-world usage across multiple product surfaces. Sounds great – what will I do? • Develop and maintain backend services supporting AI-enabled applications. • Build service architectures and processing workflows around machine learning models. • Integrate and manage interactions with LLMs, embeddings, and other AI capabilities. • Improve system performance through techniques such as caching, batching, asynchronous processing, and streaming. • Establish and maintain effective monitoring, logging, alerting, and operational practices. • Troubleshoot complex issues across distributed services and production environments. • Work closely with engineering and AI/ML teams to bring new capabilities from development into production. Sounds perfect to me, what specifics are you looking for? • Strong software engineering fundamentals with solid backend development experience. • Experience building scalable services where performance and reliability are important. • Exposure to AI/ML systems, particularly LLM-based applications, inference workflows, embeddings, or multimodal technologies. • Comfortable working with distributed architectures and diagnosing issues in production. • Practical, hands-on approach to engineering with an emphasis on delivering and iterating quickly. • Ability to balance engineering quality, performance, scalability, and operational considerations. What Success Looks Like • Backend services remain stable and performant as AI workloads grow. • AI capabilities can be exposed through well-designed, maintainable APIs and services. • Production issues are identified and resolved efficiently with minimal disruption. • System performance, scalability, and reliability improve continuously through measurement and iteration. • New AI capabilities can be integrated into the product without creating unnecessary operational complexity. Technical proficiency across: • Python • Node.js • PyTorch • Commercial and open-source LLM platforms • SQL and NoSQL databases • Kubernetes • Docker • Cloud-based infrastructure and distributed services