Staff AI Ops Engineer

Calix

2d•Remote

About The Position

The Calix platform enables Communication Service Providers (CSPs) of all sizes to transform and future-proof their businesses. Through real-time data, automation, and actionable insights delivered via Calix One — our cloud-first, AI-powered platform — CSPs can simplify operations, collapse cost, and accelerate innovation. Calix One brings together the automation of everything and the experience of one, empowering customers to deliver differentiated subscriber experiences while driving acquisition, loyalty, and revenue growth. This is the Calix mission: to enable CSPs of all sizes to simplify, innovate, and grow, strengthening both their businesses and the communities they serve. We’re at the forefront of a once in a generational change in the broadband industry. Join us as we innovate, help our customers reach their potential, and connect underserved communities with unrivaled digital experiences. Calix is where passionate innovators come together with a shared mission: to reimagine broadband experiences and empower communities like never before. As a true pioneer in broadband technology, we ignite transformation by equipping service providers of all sizes with an unrivaled platform, state-of-the-art cloud technologies, and AI-driven solutions that redefine what’s possible. Every tool and breakthrough we offer is designed to simplify operations and unlock extraordinary subscriber experiences through innovation. Calix is seeking a highly skilled Staff 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. Please note that as part of the recruitment and hiring process, there is an in-person meeting that will take place.

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 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

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