Hire a DevOps engineer with A.Team
Senior DevOps and platform engineers in A.Team's network ship across AI infrastructure, enterprise, and financial services. Outcome-scoped engagements, North American working-hour coverage.
Updated April 21, 2026
Jason M.
Senior DevOps / Platform Engineer
12 yrs exp.
Valerie P.
Senior DevOps / Platform Engineer
9 yrs exp.
Mike C.
Senior DevOps / Platform Engineer
11 yrs exp.
A.Team places senior DevOps and platform engineers from a curated, invite-only network of builders. Describe the engagement, get a curated shortlist of two to three matched engineers within 72 hours, interview on your terms, and have a builder embedded in your team by week one. Engagements run three to twelve months, either solo or on a platform team with a managing partner who owns delivery. Pricing is outcome-scoped, not hourly.
What a DevOps engineer hired through A.Team looks like
A typical A.Team DevOps or platform engineer has eight to twelve years of production experience managing infrastructure, CI/CD pipelines, and cloud platforms at companies past the Series B stage. The tech mix across missions skews toward AWS and Kubernetes with Terraform for infrastructure-as-code, Golang for high-performance data processing components, and cloud cost optimization work (Glow's "Cloud Cost Optimization" mission makes the cost-savings angle explicit). Missions span standalone DevOps embeds (Flux, OneLayer, Simpson Strong-Tie), small platform teams (Plato Systems, Bespoke Labs, Baton), and AI infrastructure work where the DevOps engineer manages the compute environment that AI systems run on.
The missions reflect an important pattern in modern DevOps work: a meaningful share involve AI infrastructure, managing GPU clusters, orchestrating distributed ML workloads, or building the platform that serves model inference at scale. A.Team's DevOps network includes engineers who've operated at that intersection.
A recent example: Terr.ai, a property management platform, worked with an A.Team engineer to build the backend infrastructure and API layer connecting distributed AI agents that automatically handle tenant communications. The work required building a scalable Python backend capable of processing 600+ tenant messages per property manager per month, integrating OCR and visual LLMs for lease document processing, and orchestrating voice and text AI agents across multiple communication channels. The system architecture, multi-channel AI agent orchestration running on scalable infrastructure, required thinking about reliability, throughput, and future scaling from the start. The result: property managers went from reactive inbox management to a proactive AI system that handled routine inquiries automatically and flagged exceptions. That shape of work, building reliable infrastructure for AI-native products, represents the modern DevOps and platform engineering remit in A.Team's mission set.
Why the quality is there
A.Team's builder network is invite-only, with admission running about two percent of applicants. DevOps and platform engineers enter through delivery history in production infrastructure, cloud platforms that real products ran on, CI/CD pipelines that real teams shipped through, and cost structures that real finance teams scrutinized. The DevOps missions in A.Team's pipeline reflect production-grade work at companies where infrastructure reliability had direct business consequences: Flux (AI compute infrastructure), Simpson Strong-Tie (enterprise platform), Endeavor (large-scale event operations).
What the numbers say
- Missions delivered
- 10+¹
- Industries served
- AI infrastructure, enterprise, fintech
- Common engagement shape
- Solo DevOps embed; small platform team
- Tech signal
- AWS, Kubernetes, Terraform, Golang; AI/ML infrastructure
¹ Tagged missions sourced from A.Team's deal pipeline. Figures are directional and reflect trailing engagement history; actual volume is higher due to dealname parse rate.
Illustrative senior DevOps / Platform Engineer profiles
Jason M.
Senior DevOps / Platform Engineer
12 yrs exp.
Valerie P.
Senior DevOps / Platform Engineer
9 yrs exp.
Mike C.
Senior DevOps / Platform Engineer
11 yrs exp.
Lian F.
Senior DevOps / Platform Engineer
8 yrs exp.
Why A.Team for a DevOps engineer specifically
Senior-only bar, verified by delivery.
Every DevOps engineer in the network has managed production infrastructure. Configuring local environments and cloud certification courses are below the bar. The evaluation is delivery history and managing-partner reviews from real infrastructure engagements.
AI infrastructure coverage.
A growing share of DevOps missions involve AI/ML infrastructure, Kubernetes-based GPU orchestration, ML model serving, and the compute environments that AI-native products run on. A.Team's network includes engineers who've shipped at that intersection.
Scales to a platform team.
If the scope requires more than one DevOps engineer, or a combination of DevOps alongside a backend engineer and data engineer, A.Team assembles the platform team in one engagement. The managing partner runs delivery across the team.
How the engagement works
Intake.
Describe the engagement at a.team/get-started or in a 45-minute scoping call with a partner. You leave with a shared picture of the infrastructure scope and team shape that fits.
Shortlist.
Within 72 hours for single-builder engagements, or three to five business days for team engagements, A.Team returns two to three named engineers with their background, past missions, and the specific cloud and infrastructure stacks they've shipped in.
Kickoff.
Week one includes setup on your systems, a working session on the first two weeks of milestones, and alignment on infrastructure standards and deployment conventions.
Managed delivery.
The managing partner runs weekly delivery reviews. The DevOps engineer ships against the scoped infrastructure surface. Team shape adjusts if the platform scope grows.
Common role variations
Most DevOps missions fall into three shapes. Senior DevOps engineer (7+ years) is the default: AWS/GCP/Azure platform management, Kubernetes orchestration, Terraform infrastructure-as-code, CI/CD pipeline ownership, and production incident response. Platform engineer applies when the scope is building internal developer platforms, self-service deployment tools, developer experience tooling, or shared infrastructure services that multiple product teams consume. MLOps / AI infrastructure engineer applies when the work is specifically about managing the compute environment for AI/ML workloads: GPU cluster management, ML model serving infrastructure, feature stores, and ML pipeline orchestration.
The full scoping playbook is in the DevOps engineer hiring guide.
What it costs
Senior DevOps and platform engineering engagements through A.Team are outcome-scoped rather than hourly. Rate benchmarks by specialization are in the DevOps engineer rate guide, and an intake at a.team/get-started returns a shortlist and a scoped proposal.
Related roles
Common questions
The typical DevOps builder placed on a mission has eight to twelve years of production infrastructure experience, managing cloud platforms, Kubernetes clusters, and CI/CD pipelines at companies where infrastructure downtime had direct business consequences. The filter is not for engineers who've configured cloud accounts but for engineers who've operated production infrastructure at scale.
Two to three matched DevOps engineers land on your desk within 72 hours of an intake submission at a.team/get-started. Standard AWS/Kubernetes engagements land fastest; AI/ML infrastructure and GPU orchestration engagements run three to five business days.
A.Team's DevOps network includes engineers who've shipped at the AI/ML infrastructure intersection, GPU cluster management, ML model serving, and Kubernetes-based distributed AI workloads. The intake brief should specify whether the scope is traditional cloud DevOps or AI/ML infrastructure so the matching returns engineers with the right production history.
Every engagement includes a trial period at the front end. If the match needs to shift, the managing partner reshapes the team without restarting the commercial.
In practice, the roles overlap. DevOps typically refers to the discipline of managing deployment pipelines, release processes, and production infrastructure. Platform engineering refers to the practice of building internal developer platforms that abstract the infrastructure complexity from product teams. Both require the same underlying cloud and Kubernetes expertise; the distinction is whether the work faces operations (DevOps) or internal developers (platform).
People also ask
US-based senior DevOps contractors typically run $130 to $200 per hour. MLOps and AI infrastructure specialists are at the higher end of that range. Full benchmarks are in the DevOps engineer rate guide.
An FTE DevOps search takes 60 to 90 days. A contractor through a curated platform takes one to three weeks. A.Team's intake-to-working-builder timeline typically runs 72 hours for single-engineer engagements and up to two weeks for platform team builds.
A DevOps engineer typically owns the CI/CD pipeline, cloud infrastructure provisioning, and deployment tooling. An SRE (Site Reliability Engineer) focuses on production reliability, SLOs, incident response, on-call rotations, and post-mortems. At smaller companies the roles merge; at larger companies they split. A.Team's DevOps engineers tend to cover both deployment tooling and production reliability rather than specializing purely in SRE.
A.Team matches on the cloud platform you specify. AWS is the most common platform in the mission set; GCP and Azure are present. Multi-cloud and cloud-agnostic infrastructure work (Terraform, Kubernetes) appears across all platforms. The hiring guide has a cloud selection framework for companies early in that decision.