QA / E2E engineer
Seasoned QA/E2E engineer who owns end-to-end validation of the DriveNets Application Platform — from UI and REST/gRPC APIs through workflow orchestration and microservices down to configuration on real network elements — matrix-leads cross-team test efforts, and directs AI agents to author and triage tests. Key stack: Python/pytest, REST/gRPC, Docker/Kubernetes, CCNP-level networking.
- Compensation
- Not disclosed
- Location
- Raanana, Center district
- Work setup
- Hybrid
- Seniority
- Not specified
- Employment
- Contract
- Experience
- 8+ years
- Category
- Qa
- Posted
Skills
Must have
- 8+ years in QA, focused on cloud-native applications and distributed systems/microservices
- CCNP-level networking: routing/switching depth beyond fundamentals (L2/L3, IP, ARP, MAC, VLAN, DHCP, ICMP)
- CCNP certification or equivalent hands-on experience
- Strong understanding of REST and gRPC testing methodologies
- Proficiency with Python testing frameworks (pytest) and writing maintainable automated tests
- Working knowledge of containerization and orchestration (Docker, Kubernetes)
- In-depth Linux knowledge
- Experience with CI/CD pipelines
- Command of formal testing methodology and artifacts (STP / STD / STR)
- Hands-on experience with test-management tooling (e.g. TestRail) and issue tracking (e.g. Jira)
- Presentation skills (PowerPoint or equivalent) for mixed engineering and product audiences
- Ability to matrix-lead: influence without authority across teams
- BSc in Computer Science, Electrical Engineering, or related technical field, or equivalent hands-on experience
Nice to have
- Experience with workflow engines (Temporal, Airflow)
- Infrastructure-as-code experience (Terraform, Helm)
- Track record of building or heavily using AI/agentic automation in a QA context
Full description
#HybridR&D – Raanana
DriveNets builds high-performance networking solutions for large-scale AI infrastructure and service-provider networks. DriveNets’ Network Cloud powers production networks at global operators such as AT&T and Comcast, while its AI Fabric solutions optimize the performance and time to first token (TTFT) in multi-vendor AI environments at foundation model labs, NeoClouds, and enterprises. DriveNets combines high-performance networking, end-to-end automation, and full-stack AI cluster optimization to remove network bottlenecks, maximize GPU utilization, accelerate AI cluster deployments, and improve token economics
The Role
We are looking for a seasoned QA / E2E engineer who can hold the entire DriveNets Application Platform (DAP) in their head — from the UI and REST/gRPC APIs, through workflow-engine orchestration and microservices, down to configuration pushed onto real network elements — and prove with evidence that it works end to end.
This is not a single-service testing role. You will own end-to-end validation across the DAP platform and matrix-lead the work: bringing development teams, system architects, product, and other stakeholders into a shared definition of success. You will increasingly do this by directing AI agents rather than only writing tests by hand.
Responsibilities
- Own end-to-end test strategy and execution across the DAP platform — translate features and epics into complete E2E test plans that trace each requirement/acceptance criterion to a concrete test.
- Matrix-lead cross-team work: pull in developers from different teams, system architects, and product; align them on risk, coverage, and what "done" means.
- Design and execute test plans for REST/gRPC APIs and workflow-engine functionality, including dynamic task addition, orchestration, and state transitions.
- Validate configuration and provisioning flows end to end — from UI/CMS edits and config-template rendering through to the configuration applied on the target network element, confirming on-device before/after state.
- Build automated suites for contract validation, regression, and critical-path coverage; design negative and failure-mode tests, not just happy paths.
- Identify, reproduce, document, and track defects with self-contained, reproducible bug reports (exact flow, inputs, payloads, logs, on-device evidence) and drive them to verified closure.
- Present plans, risks, and results clearly — in slides and live walkthroughs — so engineering and product can decide.
- Direct AI agents (e.g. Cursor, Claude, agentic subagents) to author test plans, generate and execute tests, and triage failures — while owning correctness of the output.
Requirements
- 8+ years in QA, with a focus on cloud-native applications and distributed systems/microservices.