Lead AI Engineer (AI Systems & Automation)
New
Based in the United StatesFull-TimeLead
Salary not disclosed
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Job Details
- Required Skills
- DockerNode.jsPythonSQLKubernetesNosqlLLMDistributed Systems
Requirements
- Extensive experience in backend engineering or AI engineering within production environments.
- Proven expertise building or scaling AI-powered systems using technologies such as large language models, embeddings, recommendation engines, or workflow automation.
- Strong knowledge of distributed systems architecture and production system design.
- Hands-on experience developing AI inference pipelines and orchestration frameworks.
- Excellent debugging and problem-solving skills in high-scale, latency-sensitive systems.
- Experience implementing observability, monitoring, incident response, and production support practices.
- Demonstrated ability to lead technical initiatives across cross-functional teams while maintaining hands-on coding responsibilities.
- Proficiency with Python, Node.js, SQL and NoSQL databases, Kubernetes, Docker, and modern cloud-native development practices.
- Strong communication, ownership mindset, leadership skills, and the ability to perform effectively in globally distributed, fast-paced environments.
Responsibilities
- Lead the design, architecture, and delivery of production AI systems that automate complex business workflows.
- Develop scalable AI orchestration layers connecting large language models, backend services, and operational processes.
- Own end-to-end AI infrastructure, including inference pipelines, intelligent routing, caching mechanisms, and fallback strategies.
- Drive technical decisions focused on system performance, scalability, reliability, latency, and operational cost.
- Implement and maintain robust observability practices through monitoring, logging, tracing, and alerting solutions.
- Collaborate closely with cross-functional teams to deliver end-to-end AI-powered features and services.
- Troubleshoot complex production issues, perform root-cause analysis, and continuously improve system resilience.
- Establish engineering standards, best practices, and architectural guidelines for AI system development.
- Review code, provide technical leadership, and mentor engineers to strengthen engineering quality and execution.
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