Meet the Team
You’ll join a cross-functional AI engineering group working at the intersection of engineering, product, and data science to bring advanced AI capabilities into real-world production systems. The team focuses on building scalable AI/ML and Generative AI platforms and services that power intelligent features, agentic workflows, and data-driven products.
We operate across the full lifecycle — from early experimentation and prototyping to production-grade deployment, scaling, and long-term system reliability. Innovation, strong engineering fundamentals, and applied AI excellence are core to how we work.
Your Impact
As a Staff Machine Learning Engineer, you will play a key role in translating AI innovation into scalable, reliable systems that deliver real business value.
You will:
- Partner with engineering, product, and data science teams to convert requirements into prototypes and production-ready AI systems.
- Build, deploy, and scale AI/ML and Generative AI services, taking solutions from experimentation through full production.
- Design and operate high-performance AI systems, optimizing for inference latency, throughput, and cost efficiency at scale.
- Drive adoption of agentic AI architectures, LLM-powered features, and modern ML platform capabilities.
- Proactively identify innovation opportunities, explore emerging AI/ML approaches, and turn them into concrete technical proposals.
- Stay current with AI/ML advancements and apply relevant innovations to active initiatives.
- Influence system architecture and guide engineering best practices for ML platforms and cloud-native AI systems.
Minimum Qualifications
- Strong backend development experience in Python (preferred) or Java.
- Experience building API-driven and event-driven services for AI inference, agent orchestration, tool invocation, and data pipelines.
- Hands-on experience with Generative AI and Large Language Models (LLMs)
- Experience with agentic AI systems and agent orchestration frameworks such as LangGraph.
- Strong experience with Kubernetes and containers, including deploying and scaling ML training and inference workloads across CPU and GPU environments.
- Solid background in DevOps/MLOps and CI/CD, including automated training, evaluation, and model promotion pipelines.
- Experience with source control and CI/CD tools such as Git, Jenkins, ArgoCD, or similar.
- Hands-on experience with AWS, GCP, or Azure.
- Experience designing and operating high-performance AI/ML systems.
Preferred Qualifications
- Proven technical leadership, with experience influencing system architecture and mentoring engineers on ML platforms and cloud-native best practices.
- Strong systems-thinking ability to balance model quality, scalability, reliability, latency, and cost in real-world AI deployments.
- Demonstrated ability to identify and drive adoption of new AI/ML approaches and translate them into scalable production solutions.
- Deep interest in staying at the forefront of AI/ML and Generative AI advancements
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.
