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As part of System Architecture group, the Cybersecurity System Architect will define the end-to-end architecture of advanced network security services, such as intrusion prevention systems (IPS), and host-based intrusion detection systems (HIDS) and firewalling. This role is at the forefront of integrating deep security intelligence into high-performance, scalable network operating systems and telecom-grade platforms.
As part of R&D core function, shaping next-generation secure network infrastructure by embedding deep packet inspection, behavioral analytics, and threat mitigation into the product architecture.
Responsibilities
1. Architecture of Integrated Security Services
Define and lead the system architecture for L3-L7 firewalling, stateful inspection, policy enforcement, and application-aware filtering.
Architect integration of IPS, DPI, signature- and anomaly-based detection, and evasion-resilient detection engines into control and data plane systems.
Specify how HIDS capabilities will be embedded or interfaced with NOS components for detecting host-based anomalies and compromise indicators.
2. Threat Detection & Prevention Frameworks
Design scalable architectures that support high-speed signature matching, traffic heuristics, and flow analysis under real-world traffic conditions.
Define mechanisms for rule updates, threat intelligence feeds, and integration of ML-based detection algorithms.
Architect policy engines for complex rule matching, including user-defined policy trees and hierarchical control structures.
3. Secure System Integration
Lead system-level threat modeling and security design reviews across platform, OS, and networking protocol layers.
Define secure communication paths, trust boundaries, and cryptographic protections for sensitive metadata, logs, and update mechanisms.
Ensure proper isolation and sandboxing of inspection/control modules, especially in multi-tenant or containerized environments.
4. Performance and Resilience Considerations
Design architectures to meet line-rate security enforcement, ensuring minimal latency overhead while preserving packet integrity.
Align with the HW Architecture for performance optimized flow offload strategies (e.g. hardware-assisted DPI).

we are seeking a senior AI Researcher to join its R&D group and lead the frontier of large-scale LLM optimization. You will focus on maximizing performance, scalability, and efficiency of LLM training and inference across massive GPU clusters, bridging deep learning research, distributed systems design, and hardware-aware optimization.
At our company, we treat AI performance as a systems problem. Just as we reinvented networking through disaggregation and software-defined scale, were applying the same philosophy to AI infrastructure. Your work will directly influence how large models are deployed, scaled, and optimized across high-density compute environments.
Responsibilities
Research, design, and implement new optimization strategies for large-scale LLM training and inference (e.g., tensor/pipeline/expert parallelisms, quantization, prefill/decode disaggregation, GPU communication optimization).
Profile distributed training and inference pipelines to identify algorithmic, memory, and scheduling inefficiencies.
Collaborate closely with systems, compiler, and infrastructure teams to co-design efficient communication topologies, memory management, and runtime scheduling.
Develop internal tools for huge-scale LLM benchmarking, profiling, and automatic tuning.
Validate research through measurable impact, higher throughput, better FLOPS utilization, improved convergence efficiency, or reduced compute cost.
Present research and engineering results to internal and external technical audiences.

We are looking for an experienced System Integration engineer who specializes in service provider solutions and complex Routing/MPLS Environment.
Responsibilities:
Simulate and validate the integration between multiple routing and networking features in complex environment
Learn, investigate and write Test Plan
Run testing scripts and monitor the result in lab environment
Be an integrated part of network design for our customers.

We are looking for the best of the best data Networking QA engineers with an ability to think out of the box, execute, and turn the out of the box thinking into real value for our customers.
Responsibilities:
As a data-networking QA engineer, you will:
Be part of R&D scrum team
Work closely with RnD, Product and Architecture to define, develop and test Network features.
Drive quality improvement efforts focusing on networking aspects of newly developed features
Run manual and automated tests on complex environments
Work on linux based platforms and use automation on python environment.

abra R&D is looking for a highly curious and technically skilled Full-Stack System Developer to join our team! This role is perfect for someone passionate about understanding technology at every layer — from operating systems and network protocols to enterprise systems and software infrastructure. You will work on complex automation tools, enterprise systems, and infrastructure components, while applying system-level thinking and taking ownership end-to-end.. Responsibilities:
* Develop, maintain, and optimize enterprise systems, automation tools, and software infrastructure.
* Work closely with networking and system components, managing processes, permissions, services, and logs.
* Design and implement secure communication and protocols (TCP/IP, DNS, HTTP, Proxy, VPN, Routing, SSL/TLS).
* Integrate with enterprise systems: APIs, ERP, Databases, Active Directory.
* Monitor, troubleshoot, and optimize system performance.
* Collaborate with cross-functional teams in a dynamic environment.