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Author: Mike Vizard

Ten Great DevOps Job Opportunities

devopsy.kinsta.cloud is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted as part of an effort to better serve our audience.

Our goal in these challenging economic times is to make it just that much easier for DevOps professionals to advance their careers.

Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role, it tends to create opportunities for others.

The ten job postings shared this week are selected based on the company looking to hire, the vertical industry segment and naturally, the pay scale being offered.

We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration.

Dice

Booz Allen Hamilton
North Charleston, SC
DevOps Engineer
$77,500 to $176,000

SAP Americas, Inc.
New York, NY
Senior DevOps Engineer
$189,592 to $222,288

GovCIO
Remote
Senior DevOps Engineer
$120,000 to $130,000

University of Washington
Seattle, WA
AI DevOps Engineer
$87,624

Booz Allen Hamilton
San Antonio, TX
Platform DevOps Engineer
$61,900 to $141,000

Indeed

Costco Wholesale
Seattle, WA
DevOps Engineer — Global Business Application & Platform Services
$85,000 to $225,000

UltraViolet Cyber
Lehi, UT
DevOps Engineer
$110,000 to $130,000

Alarm.com
Centennial, CO
DevOps Engineer
$120,000 to $130,000

SpaceX
Redmond, WA
IT Systems Engineer, DevOps
$130,000 to $160,000

Akina, Inc.
Annapolis Junction, MD
DevOps Engineer 3 (Kubernetes, Terraform, AWS)
$185,000 to $252,000

May 11, 2026
Survey Surfaces Significant Levels of IDP Investment to Reduce SDLC Friction

A survey of 954 IT decision-makers suggests more resources are now being allocated to reducing friction across the software development lifecycle (SDLC).

Conducted by CDW, the survey finds more than two-thirds of respondents (68%) report their organization has adopted an internal developer platform (IDP). The primary goal is to improve operational efficiency (57%), provide better user experiences (48%), and improve observability and security (47%), the survey finds.

However, a significant percentage of respondents also noted that their development teams are still encountering friction, with systems integration (25%) and security and compliance restrictions (23%) identified as the two primary sources.

Additionally, the survey identifies testing and quality assurance (22%) and integration, deployment and implementation (18%) as the two biggest bottlenecks in their organization’s software engineering workflows.

IT leaders, as a result, are investing more in automation in areas such as security (47%), testing (45%) and monitoring (43%), the survey finds.

Marc Litten, senior manager for AI factory and data center strategy at CDW, said artificial intelligence (AI) is clearly a significant element of any approach to automation. More than two-thirds of survey respondents (67%) said their organization is spending significantly on generative AI. The issue is only 31% said they have achieved moderate success, which suggests many organizations are still struggling with how to operationalize it, noted Litten.

While many individuals within organizations have been able to automate a range of tasks, organizations are now appreciating the amount of effort and expertise that is needed to automate actual workflows, he added.

Additionally, as more AI agents are deployed, organizations are not only contending with token costs but also AI agent sprawl, he added. At its core, AI is not necessarily so much a tool problem so much as it is a people and process issue, said Litten. The simple truth is rather than falling behind most organizations are encountering the same challenges at relatively the same pace, he noted.

DevOps teams are, of course, generally at the forefront of agentic AI adoption. Therefore, many of the governance and security issues that organizations encounter are first experienced by their software engineers. The one thing that is certain, however, is that software engineering workflows are now evolving more rapidly than ever.

Hopefully, the end result of that change will be higher quality software. In the short term at least, there appears to have been more emphasis placed on generating code faster. Many DevOps teams, as result, have been overwhelmed by the volume of code generated by AI coding tools, with much of that code for one reason or another not actually making it into a production environment.

Eventually, however, the quality of code being generated by AI coding tools will continue to improve. In the meantime, DevOps teams would be well-advised to review existing code review and testing capabilities with an eye toward relying more on AI agents to validate the massive volumes of code being generated by AI agents that depend on large language models (LLMs) trained on flawed code initially created by human developers that might not always reflect best practices.

May 8, 2026
The Messy Reality of Vibe Coding

The default reaction to vibe coding has been alarm — a default assumption that letting AI write large chunks of an application is going to flood production with vulnerabilities and undocumented behavior. That fear is doing as much damage as the bad code people are afraid of. Teams that freeze, ban the tools or push the work into the shadows end up with less visibility into how AI is actually showing up in their codebase, not more.

Tyler Merritt, CTO at UneeQ, joins Mike Vizard to push back on the panic and reframe the problem. Merritt’s argument is that AI-assisted development is a construction site, not a finished building — and construction sites are inherently messy. The job for engineering leaders isn’t to keep the site spotless, it’s to make sure the right safety systems, inspections and review steps are wrapped around the work that’s happening anyway.

They get into the practical mechanics of doing that. Instead of trusting any single model, Merritt makes the case for using multiple AI assistants — Claude, Gemini and others — as a kind of cross-check, where one model reviews what another produced and pulls weaknesses to the surface before they hit a pull request. Pair that with the existing toolchain (SAST, dependency scanning, code review, tests) and the AI output starts to look more like any other developer’s output: imperfect, but reviewable.

The longer-term view is more optimistic than the headlines suggest. Merritt points out that no developer wakes up wanting to ship insecure code, and as these assistants get better at understanding context, security and intent, they have a real shot at making the secure path the easy path — turning today’s messy reality into a faster, safer way of building software.

May 7, 2026
Survey Sees AI Driving DevOps Productivity Gains Despite Challenges

A global survey of 636 software development professionals published today finds nearly two-thirds (64%) believe they are achieving at least a 25% increase in developer velocity and productivity using artificial intelligence (AI).

Conducted by Jellyfish, a provider of a software engineering intelligence platform, just under a quarter (24%) report there has been a 50% to 100% increase in developer velocity and productivity, while another 6% have seen an increase of 100% or more.

The top use cases for AI are code writing (53%), code review (49%) and code explanation (43%), with Claude Code (39%), Gemini Code Assist (35%) and GitHub Copilot (31%) being the top three tools adopted.

However, only slightly more than half (53%) said AI is improving the quality of the code being developed. Other challenges include increasing cost of AI tools (42%), reluctance in adoption from senior engineers (36%) and a proliferation of tools making it difficult to select the best one (31%).

Despite these issues and concerns, a full 80% said AI is a positive on productivity, with three-quarters of respondents (75%) said AI has made more time available to focus on high-value activities. For example, 46% said they expect to be able to spend more time on creating roadmaps for projects. Well over half (57%) said AI increases their job satisfaction.

Adam Ferrari, a software engineer who serves as a Jellyfish advisor, said it’s apparent that AI is automating many of the tasks that previously conspired to burn out many software engineering teams. In general, there is now more joy being experienced because teams are able to spend more time solving problems for the business versus having to manually complete rote tasks, he added.

While it’s clear AI tools are having an impact on software engineering, the level of skills and expertise that teams have is uneven. In fact, only 43% characterized their adoption of AI tools as high (33%) or very high (10%), compared to another third (33%) that described their adoption as being medium.

As such, gaps are starting to emerge between organizations that have mastered the level of prompt and context engineering required to succeed when using AI coding tools and those that are still experimenting, noted Ferrari.

There also may be other factors having an impact on productivity. For example, the percentage of respondents (91%) that work for organizations that have seen an increase in the last 12 months is far higher than the percentage of organizations seeing an increase attributed to AI. In fact, 84% noted that productivity is a top management concern, with more than three quarters (78%) reporting accountability has increased in the past 12 months.

Additionally, nearly two-thirds (64%) said they still need more or different data to diagnose issues around engineering productivity. Overall, 55% of the companies surveyed use a software engineering intelligence tool. A total of 39% noted there is a dedicated team for platform capabilities and only 12% have fully adopted an internal development portal (IDP).

AI, undoubtedly, is changing the relationship between software engineering and the rest of the organization. The challenge and the opportunity now is how best to move forward as the number of AI agents that are embedded into DevOps workflows continues to multiply.

May 7, 2026
IREN to Acquire Mirantis to Reduce IT Infrastructure Management Friction

IREN Ltd, a provider of cloud infrastructure services, today revealed its intent to acquire Mirantis, a provider of open source OpenStack and Kubernetes software that is deployed in both cloud computing and on-premises IT environments.

Under terms of the agreement valued at $625 million, Mirantis will operate as an independent subsidiary of IREN, a former provider of bitcoin mining services that now specializes in hosting artificial intelligence (AI) workloads.

Dominic Wilde, senior vice president of marketing for Mirantis, said that while Mirantis will continue to engage with the more than 1,500 IT organizations it supports directly, the combined entity will reduce friction as IT teams move to deploy AI workloads that require deeper levels of infrastructure integration. That level of alignment will benefit IT organizations that are customers of both companies, he added.

Privately held, Mirantis has long provided a distribution of OpenStack and more recently has been curating a distribution of Kubernetes for cloud-native application environments. Most recently, Mirantis launched a k0rdent AI platform to provide a control plane to integrate the management of infrastructure across bare metal, virtual machines and Kubernetes environments. Earlier this year, Mirantis also made available a reference architecture for building and deploying AI workloads on Kubernetes clusters that is based on the k0rdent AI platform.

The overall goal is to make it simpler to build multi-tenant environments spanning multiple classes of processors using a set of reusable templates spanning compute, storage, networking and graphical processor units (GPUs) from NVIDIA, AMD and Intel.

IREN, meanwhile, will be able to leverage those frameworks to deploy workloads faster in a way that also serves to optimize workload performance and reduces total costs.

It’s unclear to what degree the management of infrastructure will further unify in the age of AI, but there is a growing amount of cost sensitivity. The underlying graphical processor units (GPUs) needed to run AI workloads remain scarce. Paradoxically, overall GPU utilization rates remain relatively low, creating an opportunity to use platforms like Mirantis to distribute AI workloads across servers and clusters in a way that should reduce the total IT costs.

There is, of course, no shortage of providers of distributions of Kubernetes and OpenStack platforms. The challenge has always been determining which providers of these distributions have the best tools and frameworks that DevOps teams need to deploy and manage these open source platforms at scale.

Unfortunately, many IT teams lack the IT infrastructure management expertise required to deploy and manage AI workloads. In fact, many organizations are underestimating the total cost of AI by not factoring in the amount of IT infrastructure that will be required to run these types of workloads at scale. As the pressure to operationalize AI continues to increase, the number of infrastructure challenges that IT teams will encounter will only continue to expand, especially as responsibility for managing the IT infrastructure needed to run these application shifts from data science to IT operations teams. The issue then becomes not just finding the best way to streamline the management of that IT infrastructure, but also justifying the level of investment that is going to ultimately be required.

May 5, 2026
Incredibuild Unveils Islo Sandbox to Isolate AI Coding Agents

Incredibuild this week developed a sandbox, dubbed Islo, that makes it possible to safely run artificial intelligence (AI) coding agents.

Company CEO Shimon Hason said Islo provides an isolated execution environment that enables DevOps teams to limit access to sensitive data, codebases, resources and services. Each AI coding agent is then provided with its own dedicated, isolated environment that operates independently and can be centrally managed by a DevOps team.

DevOps teams can deploy Islo independently of the Incredibuild platform or, alternatively, use the Incredibuild software development lifecycle (SDLC) management platform to apply policy controls, manage agent identities, enforce guardrails, enable observability, ensure performance and, most importantly, control costs, said Hason.

Collectively, these capabilities provide the added benefit of also making it simpler to assign long-running tasks to an AI agent that can be governed and managed without an application developer needing to be physically sitting in front of their machine, he added.

At the same time, Incredibuild also revealed today that it is partnering with the Harbor Framework community, a provider of open-source infrastructure for authoring and executing agent benchmarks and evaluations. Benchmark authors and engineers can run their tasks on Islo’s cloud sandboxes with a single configuration change to create reproducible environments that can execute tests in parallel.

Now that using AI to write code is all but a solved issue, the next major challenge is to ensure that the tools being used are being effectively managed, said Hason. The overall goal is to make it simpler to safely deploy AI coding agents at scale in a way that ensures DevOps teams are able to maintain control over the application development environment, he added.

It’s not clear to what degree organizations are now relying on AI coding tools to generate code that actually winds up in a production environment. A recent Futurum Group survey, however, found a full 60% of respondents said their organization is now actively using AI to build and deploy software. The top areas of investment over the same period are AI Copilot/AI code tools (38%), AI agent development (37%), AI-assisted testing (37%) followed closely by DevOps (37%), automated deployment (34%) and software security testing (31%).

Regardless of approach to application development in the AI era, the one thing that is clear is legacy approaches to managing software engineering are not going to scale to the level AI coding tools and associated agents will require. The challenge then becomes determining how best to modernize platforms and workflows in a way that eliminates bottlenecks rather than creating additional ones for application developers to navigate.

Hopefully, there will come a day when rather than simply generating more code, the pace at which applications are being developed and deployed will substantially improve in the AI era. The issue, of course, is that when it comes to building and deploying an application, the actual effort required to write the code required is only a relatively small element of a much larger complex workflow that in many organizations still desperately needs to be automated.

May 5, 2026
Ten Great DevOps Job Opportunities

devopsy.kinsta.cloud is now providing a weekly DevOps jobs report through which opportunities for DevOps professionals will be highlighted as part of an effort to better serve our audience.

Our goal in these challenging economic times is to make it just that much easier for DevOps professionals to advance their careers.

Of course, the pool of available DevOps talent is still relatively constrained, so when one DevOps professional takes on a new role, it tends to create opportunities for others.

The 10 job postings shared this week are selected based on the company looking to hire, the vertical industry segment and naturally, the pay scale being offered.

We’re also committed to providing additional insights into the state of the DevOps job market. In the meantime, for your consideration.

Dice

Booz Allen Hamilton
Arlington, VA
DevOps Engineer
$62,000 – $141,000

Marsh & McLennan Companies
White Plains, NY
DevOps Engineer
$76,800 – $134,300

Vantor
Melbourne, FL
DevOps Engineer
$92,000 – $195,000

WaltaSoft Technologies
Philadelphia, PA
SRE devops engineer(F2F Interview)
$60 – $70

MphasiS Corporation USA
Columbus, OH
DevOps Engineer
$100,000 – $120,000

Indeed

SpaceX
Redmond, WA
IT Systems Engineer, DevOps
$130,000 – $160,000

Quantum US
Centennial, CO
DevOps Engineer
$130,000 – $160,000

Ascendion
Columbus, OH
Cloud DevOps Engineer
$100,000 – $120,000

Farsight AI
New York, NY
Cloud Infrastructure Engineer
$140,000 – $180,000

WilsonCTS
Charlotte, NC
Lead DevOps Engineer
$130,000 – $160,000

May 4, 2026
Arm Adds Free Toolkit to Analyze AI Agent Performance

Arm this week made available a free toolkit for analyzing agentic artificial intelligence (AI) workloads as they are being developed by DevOps and platform engineering teams.

Earlier this year, Arm unveiled a 3nm processor based on its Neoverse V3 architecture that is specifically designed for AI workloads. The Arm Performix toolkit provides system-wide analysis across metrics such as memory bandwidth, latency, cache efficiency and CPU utilization for workloads running on that processor. Additionally, Arm has included recipes for testing multiple classes of agentic AI workloads.

Developed in collaboration with Microsoft, MongoDB, Redis and SAP, the Arm Performix toolkit surfaces expert performance analysis that is designed to be easily integrated into an existing software development lifecycle (SDLC).

At the core of the Arm Performix toolkit is a Model Context Protocol (MCP) Server that enables either a human developer or an AI assistant to run Performix directly from tools such as GitHub Copilot, Kiro, Gemini and Codex.

Alex Spinelli, senior vice president for AI and developer platforms at Arm, said the goal is to create a tight feedback loop where performance evaluation becomes embedded within the software development lifecycle (SDLC). Regardless of whether the Arm Performix toolkit is invoked by a human developer or an AI agent, the need to be able to track metrics will prove to be crucial in the age of AI, noted Spinelli.

Mitch Ashley, vice president and practice lead for software lifecycle engineering at the Futurum Group, said performance analysis is moving into the agentic build loop, callable by the same agents that write and test the code. Arm’s Performix toolkit reflects hardware vendors reaching upstream into the SDLC, with the MCP Server form factor positioning agents and humans at the same interface, he added.

Vendors that ship developer tooling without an MCP-native interface will be invisible to the agents driving build and test workflows. Performance, security, and quality tooling are converging on the same form factor, and that convergence determines who gets called during the build, noted Ashley.

It’s still early days so far as adoption of an Arm AI processor is concerned, but Meta, OpenAI, Cloudflare, and SAP have already signaled their intent to adopt it. Arm is betting that by providing free tools for analyzing agentic workloads more software engineering teams will follow suit.

Ultimately, AI agents will prove to be yet another application that software engineering teams will build, deploy, update and manage. In some cases, those AI agents will need to run on a graphical processor unit (GPU) or AI accelerator but more often than not they will run on traditional processors. That latter approach should prove to be, after all, more cost effective as thousands of AI agents are deployed across the enterprise.

In the meantime, software engineering teams will undoubtedly be relying more on AI coding tools to build AI agents that will be deployed via DevOps pipelines. The challenge now is identifying the providers of tools and platforms that will make it as simple as possible to fulfill that mission at levels of scale that, from a historical perspective, will soon prove to be unprecedented.

April 30, 2026
LocalStack Adds Ability to Visually Debug AWS Apps on Local Machines

LocalStack today announced it has extended its ability to simulate Amazon Web Services (AWS) environments to provide an ability to debug applications before deploying them.

Colin Neagle, head of marketing for LocalStack, said App Inspector makes it possible for developers to debug their applications running in a simulated AWS environment inside a container on a local server.

Simulating the full application stack within a local sandbox container makes it possible to better understand application behavior such as data flows between AWS services, event execution paths and resource dependencies that may have been inadvertently misconfigured, noted Neagle.

Once discovered, App Inspector then generates a visual representation of the interaction between services in the local environment to make it simpler to debug applications without digging through logs and then needing to upload a fix to a staging server running in the AWS cloud.

That capability doesn’t replace the need for an observability platform but it does reduce the overall amount of friction software engineering teams are likely to encounter as they build applications on a laptop or local server, said Neagle.

In general, application developers still prefer to build software on a local machine that they have more control over. LocalStack was created to provide a local instance of an AWS environment that enables developers to build software that is destined to run in a cloud environment. The goal is to reduce the number of instances where code that runs on a local machine doesn’t actually run as intended in the cloud.

At present, LocalStack supports AWS, but the company is also building a set of emulation services for Microsoft Azure that will be made available later this year.

It’s not clear how much code is developed first on a notebook, desktop PC or local server, but in the age of artificial intelligence (AI) the amount of code being generated by individual developers is increasing exponentially. Every time code has to be sent back for revision, it slows the overall pace of application development. DevOps and platform engineering teams will need to streamline workflows in a way that ultimately improves productivity.

The speed at which DevOps teams make that adjustment will naturally vary from one team to another, but the sooner everyone realizes that a piece of AI-generated code might not run as intended in a cloud computing environment the less stress there is likely to be for all concerned. The challenge and the opportunity, of course, is identifying those points of friction before the amount of code being created might soon become too overwhelming to effectively deploy and manage.

Ultimately, DevOps teams need to meet application developers where they proverbially are rather than forcing them all to develop applications in the cloud. The simple truth is many developers, if inclined, will simply evade any cloud mandate using shadow IT resources. The better part of valor then is to enable application developers to build applications on the platforms they prefer in ways that can still be centrally managed by a DevOps team.

April 29, 2026
Embracing the MCP Suck: Taming the Wild West of AI Protocols

The Model Context Protocol (MCP) is moving faster than the developer community can keep up with, racing past its original design parameters and leaving teams scrambling to build clients that can match its pace. The result is an ecosystem where the protocol itself keeps shifting under everyone’s feet, and where the tooling, conventions and security thinking that should accompany a foundational standard are still being figured out on the fly.

Joey Stout, solutions architect at Spacelift, joins Mike Vizard to make the case that this is the price of being early. Stout describes an environment that increasingly resembles a Wild West, where rogue MCP servers get spun up inside organizations without anyone in leadership knowing they exist, let alone whether they have basic guardrails wrapped around them. The convenience of standing one up in a few minutes has outrun the discipline needed to govern them.

MCP servers can give AI agents broad reach into internal systems, data and APIs, and most of the early implementations were never designed with adversarial behavior in mind. Without authentication standards, scoped permissions and observability built in, every new server becomes another piece of shadow infrastructure that can be exploited, misconfigured or simply forgotten until it causes a problem.

Stout’s advice for developers is blunt: embrace the suck. The protocol is going to keep changing, the security story is going to keep evolving, and waiting for a stable, fully governed version before getting hands-on isn’t a realistic option. The teams that learn to wrangle MCP now — messy edges and all — will be the ones positioned to set the patterns everyone else ends up following.

April 27, 2026