Network Engineer, Deployment & Integration

Posted 4 Days Ago
4 Locations
In-Office or Remote
150K-250K Annually
Mid level
Artificial Intelligence • Software
The Role
The Network Engineer will deploy and validate AI datacenter network infrastructure, manage hardware logistics, troubleshoot issues, and provide operational support while ensuring production standards.
Summary Generated by Built In
About Fluidstack

At Fluidstack, we’re building the infrastructure for abundant intelligence. We partner with top AI labs, governments, and enterprises - including Mistral, Poolside, Black Forest Labs, Meta, and more - to unlock compute at the speed of light.

We’re working with urgency to make AGI a reality. As such, our team is highly motivated and committed to delivering world-class infrastructure. We treat our customers’ outcomes as our own, taking pride in the systems we build and the trust we earn. If you’re motivated by purpose, obsessed with excellence, and ready to work very hard to accelerate the future of intelligence, join us in building what's next.

About the Role

Fluidstack is seeking a Network Engineer to join our Deployment & Integration team. This is a hands-on execution role focused on building and validating AI datacenter network infrastructure at scale. You'll be in the field turning up modern datacenter fabrics - configuring switches, validating physical layer connectivity, coordinating with cross-functional teams to resolve blockers, and ensuring production-ready handovers to operations.

This role is ideal for engineers who thrive in fast-paced environments and want deep exposure to large-scale datacenter deployments. You'll work closely with senior engineers who will provide technical guidance and structured onboarding while you develop expertise in AI fabric turn-up and deployment execution. Success means independently owning pod deployments, becoming the go-to person for field execution, and growing into deployment leadership roles as the organization scales.

Focus
  • Deployment Execution: Deploy and validate datacenter network infrastructure including front-end fabric, back-end fabric, BMS, and management networks. Configure switches, install and validate optics, coordinate fiber/cabling work, and drive deployments through completion. Own the hands-on work that turns designs into production networks.

  • Physical Layer Validation: Ensure physical connectivity meets production standards. Coordinate with structured cabling teams on fiber remediation, validate insertion loss and OTDR traces, troubleshoot optical layer issues, and document physical infrastructure as-builts. You'll become an expert at diagnosing and resolving the physical layer problems that block deployments.

  • Hardware Lifecycle Management: Manage hardware logistics including device staging, rack/stack coordination, RMA processes, and DCIM updates. Track hardware inventory, coordinate vendor shipments, and ensure devices are ready when deployments need them. Own the hardware pipeline that keeps deployments moving.

  • Cross-Functional Coordination: Partner with DC Operations (rack/stack, power, cabling), ICT teams (fiber validation), Hardware teams (logistics), and Network Engineering (config validation) to drive deployments forward. You'll learn to identify blockers early, escalate decisively, and keep complex multi-team efforts on track.

  • Documentation & Process Improvement: Maintain accurate documentation of deployment activities including cutsheets, as-builts, validation results, and lessons learned. Identify gaps in deployment procedures and propose improvements. Contribute to the deployment playbook that enables the team to scale.

  • Operational Support: Provide backup operational support during and after deployments. Respond to incidents, execute troubleshooting procedures, and coordinate break-fix activities. Build the operational knowledge that makes you effective beyond just deployment execution.

About You
  • Datacenter Networking Foundation: 3-7 years in network engineering with hands-on datacenter experience. You understand modern datacenter fabrics (EVPN/VXLAN, BGP, CLOS architectures) and have configured production network infrastructure. You're comfortable with CLI, configuration management, and network validation procedures.

  • Hands-On Execution Mindset: You thrive in field environments and enjoy the satisfaction of building physical infrastructure. You're equally comfortable pulling cable, configuring switches, and troubleshooting optical layer issues. You don't need perfect tooling to get started - you execute with what's available and improve incrementally.

  • Strong Troubleshooting Skills: Methodical approach to diagnosing network issues across physical and logical layers. You can read OTDR traces, validate insertion loss, debug BGP sessions, and trace connectivity through complex topologies. You know when to dig deeper and when to escalate.

  • Coordination & Communication: Clear communicator who can work effectively across technical and non-technical teams. You've coordinated work with datacenter operators, vendors, and internal teams. You document your work clearly and follow through on commitments.

  • Self-Directed Learning: You learn quickly from structured guidance and can apply that knowledge independently. You ask good questions, seek out documentation, and take ownership of ramping up on new technologies. You see gaps in your knowledge as opportunities, not barriers.

  • Travel Ready: Comfortable with 70-80% travel to onsite deployments (weeks to months) at datacenter locations. You understand that deployment work means being wherever the infrastructure is being built.

Nice to Haves

  • AI Fabric Experience: Exposure to AI/ML networking environments with RDMA (RoCEv2), lossless Ethernet (PFC, ECN), or high-performance compute fabrics. You understand the precision and validation required when every packet matters.

  • Vendor Platform Knowledge: Hands-on experience with Arista, Juniper, or NVIDIA networking platforms. Familiarity with vendor-specific troubleshooting, TAC escalation processes, and platform quirks that impact deployment.

  • Physical Layer Expertise: Strong understanding of structured cabling standards, fiber optics (SMF/MMF), insertion loss budgets, and optical validation tools. Experience working with structured cabling vendors and ICT teams.

  • Automation Exposure: Basic familiarity with network automation concepts, configuration templating, or scripting (Python, Ansible). You may not write automation yourself but you understand how to design deployments that can be automated.

  • DCIM/Asset Management: Experience with datacenter infrastructure management tools, asset tracking systems, or inventory management. Understanding of how to maintain accurate records during fast-paced deployment cycles.

Salary & Benefits
  • Competitive total compensation package (salary + equity).

  • Retirement or pension plan, in line with local norms.

  • Health, dental, and vision insurance.

  • Generous PTO policy, in line with local norms.

The base salary range for this position is $150,000 - $250,000 per year, depending on experience, skills, qualifications, and location. This range represents our good faith estimate of the compensation for this role at the time of posting. Total compensation may also include equity in the form of stock options.

We are committed to pay equity and transparency.

Fluidstack is an Equal Employment Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans’ status, or any other characteristic protected by law. Fluidstack will consider for employment qualified applicants with arrest and conviction records pursuant to applicable law.

Top Skills

Ansible
Arista
Bgp
Clos Architectures
Evpn/Vxlan
Juniper
Nvidia Networking Platforms
Python
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The Company
HQ: London
30 Employees
Year Founded: 2017

What We Do

Instantly reserve dedicated clusters of NVIDIA H200s and GB200s for any scale to supercharge your training and inference workflows.

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