Staff ML Ops Engineer

November 22

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Calix

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Telecommunications • SaaS • Enterprise

Calix is a comprehensive solutions provider that focuses on enabling broadband service providers (BSPs) to simplify, innovate, and grow their businesses. Through its advanced broadband platform, Calix offers technologies like 10G-PON, 5G, Wi-Fi 7, and more to improve network operations, reduce downtime, and enhance the subscriber experience. The company provides managed services such as SmartLife and SmartHome, which help subscribers operate, secure, and enhance their connected lifestyles. Calix serves diverse provider types including telcos, cable operators, and municipal utilities, helping them deliver critical broadband connectivity and transform community access to digital services. With a focus on cloud technology, analytics, and transformation guidance, Calix empowers service providers to thrive in the digital age.

1001 - 5000 employees

Founded 2000

📡 Telecommunications

☁️ SaaS

🏢 Enterprise

💰 $50M Venture Round on 2009-08

📋 Description

• Design, implement, and maintain scalable infrastructure for ML and GenAI applications. • Deploy, operate, and troubleshoot production ML pipelines and generative AI services. • Build and optimize CI/CD pipelines for ML model deployment and serving. • Scale compute resources across CPU/GPU/TPU/NPU architectures to meet performance requirements. • Implement container orchestration with Kubernetes for ML workloads. • Architect and optimize cloud resources on GCP for ML training and inference. • Set up and maintain runtime frameworks and job management systems (Airflow, KubeFlow, MLflow). • Establish monitoring, logging, and alerting for ML system observability. • Collaborate with data scientists and ML engineers to translate models into production systems. • Optimize system performance and resource utilization for cost efficiency. • Develop and enforce MLOps best practices across the organization.

🎯 Requirements

• Bachelor's degree in computer science, Information Technology, or a related field (or equivalent experience). • 8+ years of overall software engineering experience. • 3+ years of focused experience in MLOps or similar ML infrastructure roles. • Strong experience with Docker container services and Kubernetes orchestration. • Demonstrated expertise in cloud infrastructure management, preferably on GCP (AWS or Azure experience also valued). • Proficiency with workflow management and ML runtime frameworks such as Airflow, Kubeflow, and MLflow. • Strong CI/CD expertise with experience implementing automated testing and deployment pipelines. • Experience with scaling distributed compute architectures utilizing various accelerators (CPU/GPU/TPU/NPU). • Solid understanding of system performance optimization techniques. • Experience implementing comprehensive observability solutions for complex systems. • Knowledge of monitoring and logging tools (Prometheus, Grafana, ELK stack). • Proficient in at least two of the following: Shell Scripting, Python, Go, C/C++ • Familiarity with ML frameworks such as PyTorch and ML platforms like SageMaker or Vertex AI. • Excellent problem-solving skills and ability to work independently • Strong communication skills and ability to work effectively in cross-functional teams.

🏖️ Benefits

• This role may be eligible for a bonus.