Описание

DriveNets is a leader in high-scale disaggregated networking solutions, seeking an ACO QA E2E Engineer.

Responsibilities

Design, create and execute comprehensive end-to-end testing scenarios for ACO based on detailed requirements and design documents
Test ACO across its UI/UX, REST APIs, database-backed workflows and the real infrastructure it manages: switch provisioning and upgrades, inventory and topology, telemetry, alerts, configuration compliance, benchmarking and multi-tenancy
Maintain and ensure functionality across the QA environments: AI fabric topologies (leaf-spine, DNOS and multi-vendor switches), GPU servers, NetBox/SLURM/Kubernetes integrations and the ACO deployments
Validate network connectivity, configuration and performance across the AI cluster infrastructure: high-speed Ethernet (400/800G/1.6T), GPU nodes, leaf-spine topologies, VLAN/VXLAN, IP addressing, MTU, link status, routing and packet loss
Verify BGP routing functionality (neighbor establishment, route advertisement, route propagation and convergence) and EVPN/VXLAN-based isolation on data-center fabrics
Execute and analyze existing AI/HPC benchmarks (RDMA, NCCL/RCCL, MPI, RoCE, training/inference, storage) on GPU and CPU clusters through ACO's benchmarking engine; compare actual performance against established baselines
Review and understand benchmarking methodologies and test procedures for high-performance networking and collective communication technologies
Support telemetry collection and validate test data (gNMI/OpenConfig counters, syslog, GPU and NIC metrics) for accuracy, consistency and reliability against device state
Use performance analysis tools, benchmarking frameworks and diagnostic utilities to execute tests, analyze results and troubleshoot issues
Execute regression tests to verify that software updates, configuration changes and platform upgrades do not introduce new issues
Collaborate closely with development teams to investigate failures, reproduce defects with clear evidence, verify fixes and ensure system quality
Document test procedures, execution results, defects and performance metrics to maintain traceability and reproducibility; prepare clear QA reports highlighting deviations, failures and potential issues
Use AI agents in your daily work to accelerate test design, execution, evidence collection and reporting, and help grow the team's test automation

Requirements

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 (STP/STD/STR)
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: 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
GPU clusters: 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)
Agile Experience: hands-on experience in Agile work processes
Excellent interpersonal and communication skills
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
A strong team player with a collaborative mindset

Nice to have

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)
Databases: 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)

We offer

Be part of the team building DriveNets' AI Cluster Orchestrator, used to deploy and run some of the most demanding AI infrastructures
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

Hiring process

Apply now!