Full Stack Lead

Location: Tel Aviv

DriveNets is a leader in high-scale disaggregated networking solutions, modernizing how service providers, cloud providers, and hyperscalers build networks. Supporting the largest network in the world, more than half of AT&T’s backbone traffic runs on DriveNets’ Network Cloud architecture.

DriveNets is launching a new and exciting Cybersecurity business unit, focused on delivering advanced cyber solutions for Tier-1 Service Providers, leveraging DriveNets’ scale, performance, and cloud-native foundations.

The Role

We are looking for a Full-Stack Tech Lead to play a key role in building this new Cybersecurity unit from the ground up.

This is a highly impactful role for an engineer who can lead end-to-end development, own architecture, and turn advanced ideas into production-ready systems. The role combines hands-on full-stack development, technical leadership, and deep integration of LLM-based AI capabilities into cyber products.

Responsibilities

· Lead design and development of full-stack cyber security products, from frontend to backend.

· Own system architecture with focus on scale, performance, security, and reliability.

· Design and implement AI-powered capabilities using LLMs.

· Drive technical decisions and engineering best practices.

· Mentor and guide engineers through design and code reviews.

· Work closely with product and business stakeholders in a fast-growing unit.

Requirements:

Must

BSc in Computer Science or related degree, or equivalent experience.

· 6+ years of experience as a Software Engineer.

· Proven experience as a technical leader / tech lead.

· Strong backend development experience (e.g., Python, Node.js, Java).

· Strong frontend development experience (e.g., React or similar).

· Experience designing APIs and distributed systems.

· Strong system design and problem-solving skills.

· Fast self-learner with strong ownership mindset.

AI & LLM Experience

· Deep understanding of LLMs, their behavior, limitations, and how to incorporate them into production systems.

· Experience with context engineering, prompt engineering, and retrieval-based workflows (e.g., RAG).

· Ability to design AI integration workflows and incorporate LLMs into larger application logic.

Advantage

· Experience with cloud-native architectures and CI/CD.

· Experience with databases and data pipelines.

· Experience working on high-scale, performance-critical systems.

· Experience developing high-scale security solutions.

· Experience with AI evaluation, guardrails, or cost optimization.