• Design, deploy, and maintain Kubernetes infrastructure supporting AI/ML workloads • Manage containerized services, autoscaling, networking, and resource optimization • Design and build high-performance Python APIs and services using FastAPI or similar frameworks • Architect backend systems for scalability, reliability, and low latency • Build integrations between AI/ML systems and the broader Albert platform • Build and operate distributed systems that handle compute-intensive and high-throughput workloads • Design for fault tolerance, graceful degradation, and horizontal scalability • Implement async workflows, job queues, and task orchestration as needed • Architect and maintain data pipelines and storage systems supporting AI/ML workflows • Implement observability including logging, metrics, tracing, and alerting • Own system reliability—troubleshoot issues, conduct post-mortems, and continuously improve • Design CI/CD pipelines and promote automation best practices • Partner closely with ML engineers to understand requirements and deliver production-ready infrastructure • Translate ML prototypes and research code into scalable, maintainable systems