ML Ops Engineer

Meet the TeamCisco’s CTO Infrastructure & Security Innovation team is pioneering the next generation of secure and scalable platforms by leveraging advanced machine learning, deep learning, and large language model (LLM) technologies. We are a forward-looking group developing novel approaches to secure digital experiences and embedding AI/ML innovation into Cisco’s infrastructure and security strategy.Your ImpactAs an MLOps Engineer, you will design, build, and optimize our next- generation machine learning pipelines and platforms. You will collaborate with data scientists, ML researchers, and security experts to operationalize models, including LLMs, at scale. Your work will ensure reliability, automation, experimentation, and compliance across the ML lifecycle.Key Responsibilities Include Build and maintain scalable ML pipelines using Kubeflow and other MLOps frameworks for training, deployment, rollback, and monitoring. Implement and optimize CI/CD pipelines to automate ML workflows, including integrated reporting, alerting, and drift detection. Deploy and optimize LLM and deep learning models for infrastructure and security-related applications, ensuring low-latency inference at scale. Collaborate with data scientists to transform experimental models into resilient, production-grade microservices. Enable experiment tracking, model registry integration, and reproducible training and evaluation pipelines. Monitor model performance through offline experimentation and online shadow deployments or A/B testing. Establish best practices for compliance, governance, rollback, and monitoring of models in sensitive security domains. Partner with cross-functional engineering and research teams across Cisco to integrate ML into infrastructure and security solutions.Minimum Qualifications 5+ years of experience in MLOps, ML engineering, or applied AI roles. Expertise with Kubeflow for orchestrating training and inference pipelines. Strong background in operationalizing deep learning and LLM models in production environments. Experience designing microservices architectures for real-time ML applications at scale. Proficiency with Python and ML frameworks (TensorFlow, PyTorch, Hugging Face, etc.). Solid background in CI/CD for ML, observability, and scalable deployment strategies. Hands-on knowledge of cloud-native technologies (Kubernetes, Docker, GCP/AWS/Azure).Preferred Qualifications 5+ years of experience in MLOps, ML engineering, or applied AI roles. Expertise with Kubeflow for orchestrating training and inference pipelines. Strong background in operationalizing deep learning and LLM models in production environments. Experience designing microservices architectures for real-time ML applications at scale. Proficiency with Python and ML frameworks (TensorFlow, PyTorch, Hugging Face, etc.). Solid background in CI/CD for ML, observability, and scalable deployment strategies. Hands-on knowledge of cloud-native technologies (Kubernetes, Docker, GCP/AWS/Azure).Why Cisco?At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint.Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere.We are Cisco, and our power starts with you.