Senior Director of Engineering - Next-generation Network OS for Switching and routing Infrastructure-10393

Role Overview

We are seeking a Senior Director of Engineering to lead the strategy, development, and delivery of next-generation Network OS for Switching and routing Infrastructure. This leader will drive innovation through AI-assisted development practices, build high-performing teams, and ensure consistent delivery of high-quality, metrics-driven outcomes at scale.

The ideal candidate brings deep domain expertise in networking infrastructure combined with modern software development leadership and a strong track record of adopting AI/ML tools to accelerate engineering productivity and product quality.

Key Responsibilities

Leadership Profile & Strategy

· Define and execute engineering strategy for Next-gen Network OS

· Lead and mentor globally distributed engineering teams, fostering a culture of innovation, accountability, and operational excellence

· Partner with Product, Architecture, and Customer Success teams to align roadmaps with business goals and customer needs

· Drive organisational transformation toward AI-assisted engineering practices

· Strategic thinker with strong execution focus

· Data-driven decision-maker

· Passion for innovation and continuous improvement

· Collaborative and influential across organisational boundaries

· Strong advocate for engineering excellence and customer value

Engineering Excellence

· Establish and enforce quality, reliability, and performance standards

· Champion modern, data-driven engineering practices, leveraging delivery performance, developer experience, and AI-assisted productivity metrics to drive continuous improvement and high-quality outcomes

· Foster a fail-fast, learn-fast culture by encouraging rapid experimentation, iterative development, and quick feedback loops to accelerate innovation while minimizing risk

· Promote blameless postmortems and continuous learning to improve systems, processes, and team effectiveness

AI-Driven Development

· Lead adoption of AI-assisted development tools (e.g., code generation, automated testing, observability, and incident response automation)

· Integrate AI/ML into engineering workflows to:

· Accelerate development velocity

· Improve code quality and defect detection

· Enhance operational insights and automation

· Promote best practices for responsible and secure use of AI in engineering

Delivery & Execution

· Drive predictable and high-quality software releases across multiple product lines

· Ensure programs are delivered on time, within scope, and aligned with strategic objectives

· Establish robust CI/CD pipelines and DevSecOps practices

· Continuously improve engineering processes based on data and feedback

Stakeholder Management

· Communicate effectively with executive leadership and cross-functional stakeholders

· Provide regular updates on roadmap execution, risks, and performance metrics

· Represent engineering in customer and partner engagements.

Qualifications Required

· 20+ years of engineering experience, with 5+ years in senior leadership roles

· 7+ years leading large, global engineering teams, including India-based and US-based organizations.

· Proven expertise in networking technologies, including Data centre, Enterprise solutions.

· Demonstrated success leading large-scale, distributed engineering teams

· Strong track record of delivering high-quality, scalable products using metric-driven approaches

· Hands-on experience driving adoption of AI-assisted software development tools and practices

· Deep understanding of cloud-native architectures, microservices, and distributed systems

· Experience with DevOps/DevSecOps frameworks and CI/CD pipelines

Preferred Quaifications

· Familiarity with observability, telemetry, and data-driven operations

· Knowledge of security principles related to networking and infrastructure

· Bachelor’s or Master’s degree in Computer Science, Engineering, or related field

Key Success Metrics

· Improvement in engineering velocity and predictability

· Reduction in production defects and MTTR

· Adoption rate and impact of AI-assisted development tools

· Platform reliability, scalability, and customer satisfaction (SLAs, NPS)

· Employee engagement and retention within engineering teams