Meet the Team

You will be pivotal in contributing to the team responsible for designing and developing the next generation of scalable Kubernetes infrastructure with machine learning platforms that support both traditional ML and state-of-the-art Large Language Models (LLMs). This is a position for experienced engineers where you will lead the technical direction, ensuring the performance, reliability, and scalability of AI systems while collaborating closely with data scientists, researchers, and other engineering teams.

Your Impact

You will take ownership of sophisticated ML pipelines, architect scalable infrastructure, and implement standard methodologies for Infrastructure as a Code with Golang along with MLOps. Your leadership will influence the adoption of modern technologies and processes while mentoring junior engineers to strengthen the team's technical foundation. This role is perfect for someone with a deep passion for solving engineering challenges in the fast-paced AI/ML space, with a focus on delivering high-impact solutions at scale.

Core Responsibilities

As a Platform Engineer With AI/ML Experience You Will

Design and develop scalable Kubernetes-based platform components, focusing on reliability, multi-tenancy, and efficient workload orchestration.
Build and enhance platform capabilities to support AI/ML and emerging GenAI workloads (including GPU-based deployments), ensuring performance and cost efficiency.
Develop backend services and platform tooling using Golang and/or Python, following strong software engineering practices.
Drive AIOps initiative across PaaS platform by collaborating with multi-functional teams, including SREs, Software Engineers to operationalize and optimize ML models effectively.
Implement and improve observability frameworks (metrics, logs, traces) to enhance platform visibility and debugging.
Build and maintain automation frameworks and Infrastructure as Code (IaC) to improve platform consistency and operational efficiency.
Ensure platform reliability, scalability, and performance through meticulous engineering practices with operational rigor.
Participate in on-call rotations, contributing to round the clock support, incident response, troubleshooting, and continuous reliability improvements.
Contribute to design discussions and technical decisions for platform and AIOps components.
Conduct code reviews, establish standard processes, and mentor junior engineers.
Stay updated on the latest trends in AI/ML to influence platform enhancements.

Minimum Qualifications / Requirements

Experience: 5-7 years of software engineering experience, including at least 2+ years in machine learning-related roles.
Expertise in Golang or Python, with hands-on experience with Kubernetes platform Along with ML frameworks (TensorFlow, PyTorch).
Assist in the design, implementation, and day-to-day operation of a Kubernetes platform supporting microservices architecture.
Support and help improve the existing platform that hosts a large number of business applications.
Help automate legacy platforms to enable infrastructure as code, increasing efficiency and consistency in platform management.
Consistent track record in designing and deploying scalable machine learning systems in production.
Experience building CI/CD pipelines for ML workflows, including model monitoring and retraining.
Proficiency in cloud platforms and orchestration tools for distributed systems.
Strong problem-solving and debugging skills for complex, large-scale systems.
Experience in mentoring engineers and driving technical decision-making.

Preferred Qualifications / Requirements

Kubernetes and Container Orchestration:
Sophisticated understanding in Kubernetes for managing production-grade systems and ensuring scalability.
Sophisticated experience with Docker and orchestration of complex services.
Software development:
Expertise in Golang or Python
Develop & enforce secure software development lifecycle
MLOps Tools and Frameworks:
Experience with architecting and optimizing workflows using Kubeflow pipelines, KServe, Airflow, and MLflow.
Ability to design and implement efficient CI/CD pipelines for ML systems.
Large Language Models (LLMs):
Understanding of LangChain and experience designing RAG systems.
Knowledge of integrating and scaling vector databases (e.g., Pinecone, FAISS) for real-world applications.
Distributed Systems and Microservices:
Consistent record of designing and leading the development of distributed systems.
Experience with implementing robust inter-service communication patterns and solving scalability issues.

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.

Kubernetes Platform Engineer - AI Infrastructure ( Golang/Python) (5+ Years)