Member of Technical Staff Software Engineer
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Summary
Builds and operates scalable, low-latency AI inference services: deploying and tuning Kubernetes workloads, optimizing autoscaling and resource allocation, writing Python scripts and APIs, and triaging production defects. Core stack includes Docker, Kubernetes, Python, Kafka/ActiveMQ, Linux, and SQL/Redis.
You will build and operate scalable, low-latency inference services. You will deploy Kubernetes workloads, optimize autoscaling and resource allocation, develop Python scripts and APIs, investigate production defects, improve reliability, document systems, and contribute to release planning.
Responsibilities
- Implement infrastructure for high-performance, low-latency inference services
- Deploy and configure Kubernetes services
- Optimize resource allocation and autoscaling policies
- Integrate inference services with Docker and Kubernetes
- Implement multi-region deployments and disaster recovery strategies
- Develop Python scripts and APIs for inference workflows
- Validate inference accuracy and performance
- Triage and resolve service defects using logs, metrics, and traces
- Debug model deployment, orchestration, and networking issues
- Develop automated failure-detection and mitigation scripts
- Write technical documentation for infrastructure and APIs
- Track defects, enhancements, and release notes in Jira and Git
- Participate in release planning and prioritization
Requirements
- Master's degree in Computer Science or a related field
- One year of relevant software-development experience
- Docker and Kubernetes knowledge
- Java or C++ knowledge
- ActiveMQ and Kafka knowledge
- Python or Groovy knowledge
- JavaScript or TypeScript knowledge
- Linux knowledge
- SQL, OracleDB, and Redis knowledge
- Git knowledge