Lead ACO QA E2E Team

DriveNets

. Build, lead and mentor the ACO E2E QA team and own the quality of every ACO release from test planning to Go/No-Go sign-off .

Posted 10/14/2026full-timeRa'anana • IsraelSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in leading QA teams, defining and executing end-to-end test strategies, and validating complex AI networking environments. Proficient in QA automation, application testing, and managing cloud deployments while ensuring high-quality standards and effective communication across teams.

Highest-signal resume keywords

QA Team LeadershipEnd-to-End TestingAI Networking KnowledgeQA Automation (Python, pytest, Playwright/Selenium)Cloud Deployments (AWS/Azure/GCP)

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

End-to-End Test StrategyApplication TestingNetworking Knowledge (CCNP Level)AI Workload OptimizationLinux EnvironmentsSQL QueryingQA AutomationKubernetesCloud DeploymentsTelemetry Validation

Soft Skills

Interpersonal SkillsCommunication SkillsTeam CollaborationDecision-MakingAdaptability

Tools & Technologies

KubernetesSLURMAWSAzureGCPPostgreSQLPrometheusGrafanaSpirentIxia

Industry Keywords

AI NetworkingData-Center FabricsBGP RoutingEVPNMulti-TenancyQuality AssuranceAgile MethodologiesCI PipelinesObservability StacksTraffic Generators

Tech Stack

Tools & technologies

AWSAzureCloudGoogle Cloud PlatformGrafanaKubernetesLinuxPostgresPrometheusPythonSeleniumSQLSwitchingGo

About the role

Key responsibilities & impact

Build, lead and mentor the ACO E2E QA team and own the quality of every ACO release from test planning to Go/No-Go sign-off
Define E2E test strategy, test plans (STP/STD/STR) and acceptance criteria based on requirements and design documents; present them to R&D and Product
Lead manual end-to-end testing across UI/UX, REST APIs, database-backed workflows and real infrastructure
Test switch provisioning and upgrades, inventory and topology, telemetry, alerts, configuration compliance, benchmarking and multi-tenancy
Own and maintain QA lab environments, including AI fabric topologies, GPU servers, NetBox/SLURM/Kubernetes integrations and ACO deployments
Validate network connectivity, configuration and performance across AI cluster infrastructure
Verify BGP routing functionality and EVPN/VXLAN-based isolation on data-center fabrics
Execute and analyze AI/HPC benchmarks on GPU and CPU clusters; compare results against baselines and report deviations
Validate telemetry collection for accuracy, consistency and reliability against device state and live traffic
Drive defect reproduction, prioritization, root-cause investigation, fix verification and regression; publish quality reports and release-readiness metrics
Define and execute the automation roadmap toward automated API, UI and device-level test suites integrated into CI
Incorporate AI agents into test design, execution, evidence collection and reporting, and establish AI-assisted QA practices
Represent QA in planning, agile ceremonies, customer escalations and field deployments

Requirements

What you’ll need

3+ years of experience leading QA engineers (hiring, mentoring, planning, delivery) on top of 5+ years of hands-on QA
Minimum 3 years of application testing across UI/UX, database-backed data and multi-step workflows in web-based systems, including test design from detailed requirements
Networking knowledge at CCNP level or higher: L2/L3 switching and routing, BGP, VLAN/VXLAN/EVPN, MTU/MSS, ECMP, and troubleshooting with packet captures and device CLI
AI networking knowledge: data-center fabrics for AI workloads – leaf-spine and rail-optimized Clos designs, RoCEv2/RDMA, lossless Ethernet (PFC/ECN/DCQCN), 400/800G/1.6T optics and link health
Experience running, monitoring and optimizing AI workloads and benchmarks (NCCL/RCCL, RDMA, MPI, training and inference) on NVIDIA/AMD GPU servers, with schedulers such as SLURM or Kubernetes
Strong hands-on experience in Linux environments (CLI, networking stack, services, logs, scripting)
Hands-on experience working with AI agents in daily engineering work: agent skills, rules/instruction (markdown) files, MCP servers and tools, and prompt-driven workflows (e.g., Claude Code, Codex, Cursor)
QA automation experience (Python, pytest, Playwright/Selenium, REST API testing, CI pipelines)
Kubernetes and containers (kubectl, Helm, Lens/OpenLens, Argo CD; monitoring pod status, reading logs, troubleshooting)
Cloud deployments (AWS/Azure/GCP, e.g., EKS)
SQL querying and data validation (PostgreSQL)
Familiarity with network operating systems (DNOS, SONiC, Arista EOS, Junos), observability stacks (Prometheus/Mimir, Grafana/Perses, gNMI) and traffic generators (Spirent/Ixia)
Proven leadership and ability to build a team, set priorities, make decisions and own outcomes
Excellent interpersonal and communication skills with R&D, Product, Support and customers
High-level proficiency in English (both written and verbal) and clear technical writing
Self-starter who learns quickly and adapts easily to new technologies
Ability to work under tight deadlines and manage multiple priorities
Strong team player with a collaborative mindset

Benefits

Comp & perks

Build the QA team of a new product – DriveNets' AI Cluster Orchestrator
Work hands-on with state-of-the-art AI infrastructure: 800G/1.6T Ethernet fabrics, NVIDIA and AMD GPU clusters, RoCE and collective communication
Join an AI-native engineering culture where agents are part of the daily workflow
Collaborate with brilliant minds in a dynamic and supportive work environment