• Join a pioneering team transforming network engineering through agentic AI
• Access to world class tech and leading engineering teams
• Blend deep software engineering skills with AI and network expertise
• Flexible hybrid work, competitive rem and inclusive work environment

KEY SKILLS & EXPERIENCE

• Extensive hands-on experience as a Network Automation Engineer and/or Software Developer within the network engineering space.
• Demonstrated expertise in network testing, validation, and automation, including experience with AI-driven or intelligent testing frameworks.
• Strong proficiency in Python and frameworks like FastAPI or Django REST for developing APIs, automation tools, and integrations.
• Background applying data-driven, AI, or machine learning approaches to network engineering, automation, or related domains (desirable).
• Solid understanding of CI/CD, DevOps workflows, and automated testing in complex network environments.
• Experience building APIs, microservices, or distributed automation architectures.
• Excellent communication, collaboration, and documentation skills.
• A delivery-focused, innovative mindset and willingness to work across multidisciplinary teams.

• Design, build, and maintain AI-driven solutions for accelerated network testing, enabling rapid, reliable validation of complex network changes.
• Create intelligent automation tools and interactive, context-aware applications that simplify network workflows and empower engineering teams.
• Develop and integrate context-aware automation powered by MCP (Model Context Protocol) and AI agent frameworks to advance intelligent network engineering.
• Develop robust integration frameworks and self-service experiences, allowing for seamless adoption of innovative network capabilities.
• Apply data science, machine learning, and advanced analytics to proactively identify network risks, support automated decision-making, and enhance the experience of engineering users.
• Implement automated test pipelines and validation tools using CI/CD workflows to support shift-left and continuous network assurance.
• Collaborate closely with network engineers, software developers, and platform teams to embed AI/ML capabilities throughout the network lifecycle and drive adoption of next-generation test strategies.
• Document best practices, architectural patterns, and operational guides to support rapid adoption and scale of AI-driven solutions