AI Operations Engineering Technical Leader

Cisco

Remote

Quick summary

Work type
Remote
Location
Usa-Milpitas
Salary
$212,300–$275,800 / yr
Posted
17 days ago
Closes
Jun 29, 2026

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $207k
This role $244k
$155k most similar roles pay here $289k

This role pays more than 70% of similar roles. Most pay $168,262–$246,150 — the shaded band above. At the midpoint, this role pays about $244k versus about $207k for comparable roles.

Based on 240 similar postings.

Employer

About Cisco

Cisco Systems is the world''s leading networking technology company, designing and manufacturing networking hardware, telecommunications equipment, and cybersecurity solutions for businesses and governments. Industry: Networking Technology & Cybersecurity

Cisco currently has 167 open roles on FindRole.

Listed pay typically runs $165,000–$241,400 across 167 roles with salary data.

Most-posted roles

View all roles at Cisco

At a glance

TL;DR · AI Operations Engineering Technical Leader

Cisco’s AI Software & Platform Group seeks a senior ML Operations Engineer to join its global DevOps team, focusing on the production readiness and maintenance of scalable machine learning systems. This role involves designing robust ML pipelines for training, validation, and deployment, building scalable infrastructure using Kubeflow in multiple public clouds, and implementing CI/CD processes in GitHub to ensure reproducibility. The engineer will also monitor models in production for performance issues and participate in on-call rotations while collaborating closely with data scientists and product managers. Key skills include proficiency in Python, knowledge of cloud platforms like AWS, Azure, and GCP, and experience with ML libraries such as Scikit-learn, PyTorch, and TensorFlow. Ideal candidates have a background in DevOps, strong automation practices, and hands-on expertise with Kubernetes, Docker, and CI/CD tools like Jenkins or GitLab.

What you'll do

  • Design and manage robust ML pipelines for training, validation, and deployment.
  • Build scalable infrastructure using Kubeflow in multiple public clouds for ML systems.
  • Implement CI/CD in GitHub to ensure reproducibility and traceability of ML systems.
  • Monitor models in production for performance degradation, drift, and fairness issues.
  • Advocate and implement automation across the infrastructure stack using IaC principles.

What we're looking for

  • 8+ years of DevOps experience with a degree in Computer Science or related field.
  • Proficiency in Python and familiarity with ML libraries like Scikit-learn, PyTorch, TensorFlow.
  • Strong understanding of CI/CD pipelines and automation tools including GitHub.
  • Experience building scalable infrastructure using Kubeflow for ML systems in public clouds.
  • Knowledge of cloud platforms (AWS essential, Azure/GCP beneficial).
  • Implement and monitor LLM evaluation and observability solutions.
  • Participate in on-call rotation for ML Operations.

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