AI Systems Engineer

Required Qualifications

• Experience designing and delivering complex, scalable AI and backend platforms with quality, reliability, and long-term maintainability in production environments.
• Strong ownership mindset with the ability to shape product and technical direction through collaboration, clear communication, empathy, and sound engineering judgment.
• Expected to translate stakeholder needs into effective solutions while mentoring others, sharing feedback, and elevating team capability.
• Specialization in and passion for AI Systems Engineering, including one or more areas: AI/ML Systems & Agents: Production-grade LLM applications, RAG pipelines, AI agents, or multi-agent orchestration systems beyond experimental use cases. Backend & Distributed Systems: Delivered scalable microservices and event-driven architectures supporting high-volume, low-latency workloads in cloud environments. ML Ops & AI Infrastructure: Designed and operated ML pipelines, model deployment systems, and infrastructure automation using modern DevOps and cloud-native tools, ensuring reliability, observability, and cost efficiency.
• AI/ML Systems & Agents: Production-grade LLM applications, RAG pipelines, AI agents, or multi-agent orchestration systems beyond experimental use cases.
• Backend & Distributed Systems: Delivered scalable microservices and event-driven architectures supporting high-volume, low-latency workloads in cloud environments.
• ML Ops & AI Infrastructure: Designed and operated ML pipelines, model deployment systems, and infrastructure automation using modern DevOps and cloud-native tools, ensuring reliability, observability, and cost efficiency.