Calix
Overview
Calix provides the cloud, software platforms, systems and services required for communications service providers to simplify their businesses, excite their subscribers and grow their value. Calix is seeking a highly skilled
AI Ops Engineer
with hands-on experience with GCP to join our cutting-edge
AI/ML
team. In this role, you will be responsible for building, scaling, and maintaining the infrastructure that powers our machine learning and generative AI applications. You will work closely with data scientists, ML engineers, and software developers to ensure our ML/AI systems are robust, efficient, and production ready. This is a remote-based position that can be located anywhere in the United States or Canada.
Responsibilities
Design, implement, and maintain scalable infrastructure for ML and GenAI applications
Deploy, operate, and troubleshoot production ML/GenAI pipelines/services
Build and optimize CI/CD pipelines for ML model deployment and serving
Scale compute resources across CPU/GPU architectures to meet performance requirements
Implement container orchestration with Kubernetes
Architect and optimize cloud resources on GCP for ML training and inference
Setup and maintain runtime frameworks and job management systems (Airflow, KubeFlow, MLflow, etc.)
Establish monitoring, logging and alerting for systems observability
Optimize system performance and resource utilization for cost efficiency
Develop and enforce AIOps best practices across the organization
Qualifications
Bachelor's degree in Computer Science, Information Technology, or a related field (or equivalent experience).
5+ years of overall software engineering experience
3+ years of focused experience in DevOps/AIOps or similar ML infrastructure roles
Proficient in IaC, using Terraform.
Strong experience with containerization and orchestration using Docker and Kubernetes
Demonstrated expertise in cloud infrastructure management on GCP
Proficiency with workflow management such as Airflow & Kubeflow
Strong CI/CD expertise with experience implementing automated testing and deployment pipelines
Experience with scaling distributed compute architectures utilizing various accelerators (CPU/GPU)
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).
Strong proficiency in Python
Familiarity with ML frameworks such as PyTorch and ML platforms like Vertex AI
Excellent problem-solving skills and ability to work independently
Strong communication skills and ability to work effectively in cross-functional teams
#LI-Remote The base pay range for this position varies based on the geographic location. More information about the pay range specific to candidate location and other factors will be shared during the recruitment process. Individual pay is determined based on location of residence and multiple factors, including job-related knowledge, skills and experience. San Francisco Bay Area:
133,400 - 226,600 USD Annual All Other US Locations:
116,000 - 197,000 USD Annual As a part of the total compensation package, this role may be eligible for a bonus. For information on our benefits click here.
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Calix provides the cloud, software platforms, systems and services required for communications service providers to simplify their businesses, excite their subscribers and grow their value. Calix is seeking a highly skilled
AI Ops Engineer
with hands-on experience with GCP to join our cutting-edge
AI/ML
team. In this role, you will be responsible for building, scaling, and maintaining the infrastructure that powers our machine learning and generative AI applications. You will work closely with data scientists, ML engineers, and software developers to ensure our ML/AI systems are robust, efficient, and production ready. This is a remote-based position that can be located anywhere in the United States or Canada.
Responsibilities
Design, implement, and maintain scalable infrastructure for ML and GenAI applications
Deploy, operate, and troubleshoot production ML/GenAI pipelines/services
Build and optimize CI/CD pipelines for ML model deployment and serving
Scale compute resources across CPU/GPU architectures to meet performance requirements
Implement container orchestration with Kubernetes
Architect and optimize cloud resources on GCP for ML training and inference
Setup and maintain runtime frameworks and job management systems (Airflow, KubeFlow, MLflow, etc.)
Establish monitoring, logging and alerting for systems observability
Optimize system performance and resource utilization for cost efficiency
Develop and enforce AIOps best practices across the organization
Qualifications
Bachelor's degree in Computer Science, Information Technology, or a related field (or equivalent experience).
5+ years of overall software engineering experience
3+ years of focused experience in DevOps/AIOps or similar ML infrastructure roles
Proficient in IaC, using Terraform.
Strong experience with containerization and orchestration using Docker and Kubernetes
Demonstrated expertise in cloud infrastructure management on GCP
Proficiency with workflow management such as Airflow & Kubeflow
Strong CI/CD expertise with experience implementing automated testing and deployment pipelines
Experience with scaling distributed compute architectures utilizing various accelerators (CPU/GPU)
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).
Strong proficiency in Python
Familiarity with ML frameworks such as PyTorch and ML platforms like Vertex AI
Excellent problem-solving skills and ability to work independently
Strong communication skills and ability to work effectively in cross-functional teams
#LI-Remote The base pay range for this position varies based on the geographic location. More information about the pay range specific to candidate location and other factors will be shared during the recruitment process. Individual pay is determined based on location of residence and multiple factors, including job-related knowledge, skills and experience. San Francisco Bay Area:
133,400 - 226,600 USD Annual All Other US Locations:
116,000 - 197,000 USD Annual As a part of the total compensation package, this role may be eligible for a bonus. For information on our benefits click here.
#J-18808-Ljbffr