Forward Deployed Engineer

Delos Data Inc; United States; Remote; USD 152,000 - 179,000; Full time

Benefits offered by this job

Equity

Job summary

Delos Data Inc. seeks an experienced infrastructure-focused engineer to own the technical success of production deployments inside customer AI datacenters.

The role blends hands-on cluster management, troubleshooting, and performance work, with feedback loops to product teams and direct collaboration with customers. You will manage deployments across GPU/XPU clusters, lead upgrades and tuning efforts, and craft automation and runbooks using Python, Bash, and APIs.

Qualifications

• Five+ years in infrastructure engineering, SRE, platform engineering, or similar hands-on roles with customer exposure.
• Experience operating AI datacenter clusters, including accelerators, high-speed interconnects, scheduling, and training or inference workloads.
• Strong Linux, container, and API skills, with ability to debug distributed systems and write production-quality Python and Bash tools.
• Practical experience with infrastructure platforms such as Kubernetes, Slurm, Tinkerbell, Canonical MaaS, or similar systems.
• Ability to communicate clearly with engineers and senior infrastructure stakeholders; willing to work on-site and travel as needed.

Responsibilities

• Serve as the primary technical owner for a customer deployment across GPU or XPU clusters, networking fabrics, schedulers, and orchestration systems.
• Lead integrations, upgrades, cluster expansions, performance tuning, and root-cause investigations for production issues.
• Create environment-specific automation, runbooks, and integration guides; prototype fixes and tools using Python, Bash, or APIs.
• Work with customer infrastructure, SRE, and platform teams, translating operational needs into product requirements with engineering colleagues.
• Share structured feedback on bugs and feature gaps, and coordinate deployment milestones with product releases.
• Support technical evaluations, proof-of-concept deployments, reference architectures, and customer workshops.

Skills

Infrastructure engineering

SRE

Platform engineering

Solutions architecture

Customer exposure

Linux

Containerization

APIs

Python

Bash

Tools

Kubernetes

Slurm

Tinkerbell

Canonical MaaS

Job description

Role overview

Own the technical success of a production software deployment inside a customer's AI datacenter. The role combines hands-on infrastructure engineering, troubleshooting, and performance work with a feedback loop into product development; it also contributes field expertise to evaluations and deployments with prospective customers.

Responsibilities

• Serve as the primary technical owner for a customer deployment across GPU or XPU clusters, networking fabrics, schedulers, and orchestration systems.


• Lead integrations, upgrades, cluster expansions, performance tuning, and root-cause investigations for production issues.


• Create environment-specific automation, runbooks, and integration guides; prototype fixes and tools using Python, Bash, or APIs.


• Work with customer infrastructure, SRE, and platform teams, translating operational needs into product requirements with engineering colleagues.


• Share structured feedback on bugs and feature gaps, and coordinate deployment milestones with product releases.


• Support technical evaluations, proof-of-concept deployments, reference architectures, and customer workshops.

Requirements

• At least five years in infrastructure engineering, SRE, platform engineering, solutions architecture, or a similar hands-on role with customer exposure.


• Experience operating AI datacenter clusters, including accelerators, high-speed interconnects, scheduling, and training or inference workloads.


• Strong Linux, container, and API skills, plus the ability to debug distributed systems and write production-quality Python and Bash tools.


• Practical experience with infrastructure platforms such as Kubernetes, Slurm, Tinkerbell, Canonical MaaS, or similar systems.


• Ability to communicate clearly with engineers and senior infrastructure stakeholders; willing to work on-site and travel as needed.

Nice to have

• Experience embedded with a major customer or launching early-stage products in large AI environments.


• Familiarity with inference serving, GPU utilization, KV-cache behavior, or disaggregated serving.


• Experience with Prometheus, Grafana, OpenTelemetry, or infrastructure analytics.

Benefits and work setup

• On-site at a customer office in Cambridge, UK, with travel as needed.


• Target base salary of £115,000-£135,000 per year, plus equity.