AI Ops Engineer
Job Description
Position: Staff AI Ops Engineer
About Calix
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 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.
Position Overview
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 build, scale, and maintain 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.
Key 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
- Set up and maintain runtime frameworks and job management systems (Airflow, Kube Flow, 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)
- 8+ years: of overall software engineering experience
- 3+ years: of focused experience in Dev Ops/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
Compensation
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: $156,400 - $265,700 USD Annual
All Other US Locations: $136,000 - $231,000 USD Annual
As part of the total compensation package, this role may be eligible for a bonus.
Seniority Level
Mid‑Senior level
Employment Type
Full‑time
Job Function
Engineering and Information Technology
Industries
Computer Hardware Manufacturing, Computer Networking Products, and Software Development