Network Engineer

TekWissen ®; Ashburn (VA); On-site; USD 120,000 - 150,000; Full time

Job summary

TekWissen Group seeks a networking automation engineer to support global datacenter lab fabrics, timing, and provisioning systems for accelerator teams from bring-up to production handoff. You will design L2/L3 networking, IPv6, BGP, EVPN/VXLAN, and optical test infrastructure, and implement declarative, template-driven config pipelines using Python, Jinja2, Git, and NetBox.

On-call, cross-site coordination, and cost awareness are part of the role.

Qualifications

• Proficiency in Python for network automation.
• Experience with Jinja2 templating across sites.
• Experience with config as code and Git version control.
• Knowledge of declarative intent modeling.
• Linux networking and system administration.
• Data hygiene and drift detection.

Responsibilities

• On-call lab and datacenter operations with incident response.
• Cross-functional delivery with platform teams and lab customers.
• Author and review method of procedure documents with controlled blast radius.
• Implement cost-aware provisioning and turn-down strategies.
• Collaborate on platform provisioning, observability, and network state management.

Tools

NetBox

Git

Jinja2

Ansible

Chef

Puppet

Salt

CI gates

Job description

Accenture helps the world’s leading enterprises reinvent by building their digital core and unleashing the power of AI to create value at speed for organizations across industries. With ~786,000 employees worldwide, it serves approximately 9,000 clients in 120+ countries across 40+ industries transform their operations through technology and human ingenuity. Key service lines include Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent, with advanced capabilities in AI and Data, Industry and Process, and Technology.

Role snapshot

You support the networks inside hardware validation labs around the world: the datacenter fabrics, timing, optical, and provisioning systems that let accelerator teams validate new platforms from first bring-up through production handoff. L2/L3 lab fabric design, precision timing as a service, intent based provisioning and automation, optical plus test infrastructure, lab operations and reliability, and cost takeout through decommissioning.

• L2/L3 at depth: VLANs, loop prevention, LAG, IPv4/IPv6 addressing, SLAAC and router advertisements, DHCP relay and helpers, PXE boot flows end to end.
• BGP: eBGP peering design, prefix policy, uplink preference tuning, per-rack or per-site route origination toward the border and aggregation layer.
• Datacenter fabrics: leaf/spine role design, EVPN/VXLAN overlays, migrating racks from L2 bridged to L3 routed boundaries.
• IPv6 operations: first-hop security (RA guard, address locking), DHCPv6 relay and MAC-stable allocation behavior including client identifier pitfalls, /64 per rack scale planning and fabric design
• Precision timing: PTP (IEEE 1588) design and troubleshooting, grandmaster placement, boundary and transparent clock concepts, sync quality monitoring against microsecond budgets.
• Fabrics for AI/HPC: PFC and lossless Ethernet tuning, link training, SerDes and optics basics (current generation high speed optics), awareness of how skew and tail latency hit collective communication.
• Optical and physical plant: fiber types and connectors, optical circuit switching concepts, commercial traffic generators, cable validation, fiber characterization from hardware timestamps.
• Access security: port ACLs, 802.1X/MAB flows, RADIUS, quarantine and remediation VLANs, device onboarding and registration.
• Multi-vendor fluency: deep on at least one datacenter NOS (Arista, Cisco, Juniper, SONiC) with demonstrated ability to pick up others, including API driven operation with rollback.
• Troubleshooting under pressure: packet level debugging, hardware timestamp analysis, method of procedure authorship, maintenance windows, rollback planning.

Required: automation and software

• Python for network automation: device APIs, text templating, input validation, operator CLI tooling.
• Templating: Jinja2 template design (reuse across sites and roles, safe defaults, per-port rendering from inventory).
• Config as code: distributed revision control fluency (branch, review, land, revert), config review culture, CI gates on generated config (diff review, automated land criteria).
• Intent modeling: declarative source of truth thinking where inventory plus intent renders device config, and generated config is never hand edited.
• Linux networking: interfaces, routing tables, namespaces, capture tooling, service management, cookbook style config management (chef).
• Data hygiene: inventory accuracy (roles, port maps, serial to port bindings), drift detection, reconciliation loops.

Required: operations and delivery

• Lab or datacenter operations: oncall rotations, incident response, break/fix prioritization, vendor RMAs, multi-site coordination.
• Change discipline: method of procedure authorship and peer review, blast radius control, pilot then scale.
• Cost awareness: circuit inventories, carrier coordination for turn-down, tracking recurring spend to zero with chargeback confirmation.
• Cross-functional delivery: shared ownership with platform teams (provisioning, observability), hardware bring-up teams, and lab customers, including consultative support outside the plan of record.

Preferred

• AI/HPC cluster networking: scale-up versus scale-out fabric design, RDMA capable fabrics, collective sensitivity to timing.
• Large fiber plant or optical switching operations.
• IPv6-first or IPv6-only environments at scale.
• Vendor evaluation: switch selection criteria, optics compatibility testing.

What predicts success here

This team works through internal platforms that all have direct industry analogues. We hire for the transferable skill; ramp covers the internal name. Git-backed hierarchical config management plus Jinja (Ansible/Salt/Puppet/Chef experience transfers), policy driven intent automation (declarative config pipelines transfer), graph modeled network state with diff-before-push (topology as data transfers), NetBox class inventory and asset sources of truth, in-house orchestration and topology visualization (any NMS you have operated or extended transfers), and Git class revision control with daily diff review.

Success in the first half looks like

A rack boundary migrated to routed operation with a clean demo, one provisioning flow that runs from intent with no manual CLI, timing service extended to a new cluster with monitoring attached, and every unplanned support ask tracked and categorized instead of lost in chat.

TekWissen Group is an equal opportunity employer supporting workforce diversity.