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 & highly scalable Kubernetes Platforms for microservices workload. Your leadership will be pivotal in driving the adoption and integration of both established Kubernetes platforms and emerging AI/ML technologies. You will mentor junior engineers to reinforce the team’s core technical expertise, ensuring a strong foundation in traditional container orchestration as well as modern AI-driven solutions. This role is ideal for someone passionate about tackling engineering challenges in dynamic environments, with a commitment to delivering scalable, high-impact solutions that blend proven infrastructure methodologies with innovative AI/ML advancements.
Core Responsibilities
As a Platform Engineer with AI/ML Experience you will:
Architect and design scalable Kubernetes platforms supporting both traditional and Large Language Models (LLMs).
Provide On Call & client support for all Kubernetes platforms
Participate in troubleshooting the Operational Issues and drive Upgrades.
Proficient in Kubernetes (K8) platform to design, develop, and maintain scalable software solutions.
Drive cross-functional collaboration across infrastructure teams to ensure seamless integration and delivery of services.
Engage directly with clients to gather IT requirements, translate business needs into technical solutions, and architect robust systems.
Drive technical brainstorming sessions with technical teams to innovate and build effective architectures aligned with client goals.
Act as a key technical liaison between clients and internal teams, ensuring clear communication and successful project outcomes.
Provide design, implementation, and operational support for a traditional Kubernetes platform tailored for microservices architecture.
Enhance and maintain the existing platform to reliably support a large portfolio of business applications.
Automate platforms to operate as infrastructure as code, improving efficiency and consistency in platform management.
Architect GPU as a Service Platform offering and provide client support for hosting AI/ML workload powered by GPU
Drive AIOps initiative across PaaS platforms by collaborating with multi-functional teams, including SRE, Software Engineers to operationalize and optimize ML models effectively.
Develop infrastructure automation tools and frameworks to improve efficiency across teams.
Ensure platform reliability, scalability, and performance through meticulous engineering practices.
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 / Requirement
Experience: 8+ years of software engineering experience, including at least 2+ years in machine learning-related roles.
Expertise in Golang or Python, with expertise in Kubernetes platform along with ML frameworks (TensorFlow, PyTorch).
Consistent track record in designing and deploying scalable machine learning systems in production.
Deep understanding of ML algorithms, data pipelines, and optimization techniques.
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:
Expertise in Kubernetes for managing enterprise grade systems and ensuring scalability.
Experience with Docker and orchestration of complex services.
Software development: Expertise in Golang or Python
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.
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.
Skills Required
- 8+ years of software engineering experience, including at least 2+ years in machine learning-related roles.
- Expertise in Golang or Python.
- Expertise in Kubernetes platform (design, develop, maintain scalable solutions).
- Experience with ML frameworks such as TensorFlow and PyTorch.
- Track record designing and deploying scalable machine learning systems in production.
- Deep understanding of ML algorithms, data pipelines, and optimization techniques.
- 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 mentoring engineers and driving technical decision-making.
- Expertise in Kubernetes for managing enterprise-grade systems and ensuring scalability.
- Experience with Docker and orchestration of complex services.
- Experience with MLOps tools and frameworks: Kubeflow pipelines, KServe, Airflow, MLflow.
- Understanding of LangChain and experience designing RAG systems for LLMs.
- Knowledge of integrating and scaling vector databases (e.g., Pinecone, FAISS).
Cisco Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Cisco and has not been reviewed or approved by Cisco.
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Healthcare Strength — Comprehensive medical, dental, and vision coverage, mental health support via an EAP, and access to on-site or virtual health centers indicate robust healthcare offerings. Wellness programs, fitness resources, and specialized services further reinforce coverage depth.
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Leave & Time Off Breadth — Generous PTO, a global minimum for paid parental leave, and unique programs like company-wide recharge days and paid volunteer time expand time-away options. Additional offerings such as Critical Time Off and adoption assistance add flexibility for life events.
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Equity Value & Accessibility — Restricted stock units and a discounted employee stock purchase plan are meaningful elements of total compensation. The prominence of equity can materially augment overall pay packages alongside salary and bonuses.
Cisco Insights
What We Do
Cisco (NASDAQ: CSCO) enables people to make powerful connections--whether in business, education, philanthropy, or creativity. Cisco hardware, software, and service offerings are used to create the Internet solutions that make networks possible--providing easy access to information anywhere, at any time. Cisco was founded in 1984 by a small group of computer scientists from Stanford University. Since the company's inception, Cisco engineers have been leaders in the development of Internet Protocol (IP)-based networking technologies. Today, with more than 71,000 employees worldwide, this tradition of innovation continues with industry-leading products and solutions in the company's core development areas of routing and switching, as well as in advanced technologies such as home networking, IP telephony, optical networking, security, storage area networking, and wireless technology. In addition to its products, Cisco provides a broad range of service offerings, including technical support and advanced services. Cisco sells its products and services, both directly through its own sales force as well as through its channel partners, to large enterprises, commercial businesses, service providers, and consumers.
