How to Survive AI — DevOps Engineers Career guide 2025
- Chandan Kumar
- 1 day ago
- 9 min read
🌊 Every Technology Wave Brings ChangeEach technology wave may make certain jobs obsolete, but it also creates tremendous opportunities. The current "AI" wave, particularly LLM (Large Language Model) technology, is no different. 🌟
🔍 From a DevOps and Platform Engineering PerspectiveThe rise of AI brings both challenges and opportunities for anyone looking to enter the field of DevOps or Platform Engineering. Here are some insights to consider:
⚠️ Note: The current labor market situation is not driven by AI; it's a continuation of the post-COVID tech bubble burst. 📉
📝 In this blog, I highlight how tools like GitHub Copilot have made core DevOps skills so easy to perform that they no longer stand out on your resume as "skills" but rather as basic expectations. The focus now shifts toward amplifying your profile with the right skills for the future. 🚀
TL;DR – What Skills Are Expected vs. What Will Amplify Your Profile
Expected (but won’t amplify your profile):
🛠️ Simple CI/CD pipeline creation
⚙️ Ansible, Terraform, Pulumi for basic orchestration
📊 PromQL, SQL for querying and monitoring
☁️ AWS, Azure, and other public cloud platforms
Critical Skills That Will Amplify Your Profile:
🧠 Troubleshooting and deep problem-solving skills
🔗 Complex CI/CD with multi-layer, multi-stack integration with IaC (Infrastructure as Code)
🖥️ Linux Administration and systems knowledge
🤖 LLMOps (DevOps for AI) – expertise in managing AI models and infrastructure
🖱️ Comfortable working with GPU ecosystems (Nvidia, AMD)
🌐 Neo-Cloud Operations – mastering hybrid and multi-cloud environments
Introduction
The technology job market, particularly within the DevOps domain, has presented significant challenges for individuals seeking to enter or advance their careers. Since 2023, a confluence of factors, including a general softening of the tech sector and rapid technological shifts, have created a sense of uncertainty and frustration for many aspiring DevOps professionals. Among these shifts, the rise of Artificial Intelligence (AI) stands out as a particularly transformative force, reshaping the very nature of DevOps roles and the skills required to succeed. While it’s true that AI-powered tools have automated and simplified certain tasks that were once the exclusive domain of DevOps engineers, this technological evolution has simultaneously opened up new avenues and created demand for even more specialized and higher-paying positions. To navigate this evolving landscape, it’s crucial to understand how AI has impacted traditional DevOps skills and where the emerging opportunities lie.
The Great Commoditization: When AI Became the Co-Pilot
The integration of AI into the DevOps workflow has led to a phenomenon of “commoditization,” where previously critical and specialized skills have become more accessible or automated through AI-powered tools. This shift has undeniably impacted the demand for certain types of DevOps roles, particularly those focused on routine automation tasks.
Following are some of the leading skill killers
CI/CD Pipeline Creation: From Jenkins days to GitHub actions
In the pre-ChatGPT era, the ability to design and implement Continuous Integration/Continuous Delivery (CI/CD) pipelines was a cornerstone skill for DevOps engineers. It required a deep understanding of various tools, scripting languages, and the intricacies of software delivery workflows.
Impact on Development Speed:
Data suggests that Copilot users experience an increase in pull requests and a higher pull request merge rate.
This indicates faster iteration and quicker releases.
In contract, back in Jenkins days, you need to know little bit of groovy in order write a decent Jenkins pipeline, GitHub Actions (similar tools GitLab, ADO) dramatically simplified the pipeline creation to the level that developers themselves could write those pipeline abolishing the need for a DevOps.
While these tools empower developers to ship faster and handle basic pipeline creation, the narrative should not focus solely on the displacement of DevOps engineers. Instead, it’s crucial to recognize that AI acts as an enabler, potentially freeing up time for DevOps professionals to focus on more complex and strategic initiatives. A solid understanding of DevOps principles and the ability to build a sturdy foundation remain paramount, with AI serving as a powerful assistant in achieving these goals.
Similar Productivity gains has been observed in various template based coding skills including
Infrastructure as Code (IaC):
Pre-AI: Writing IaC templates with tools like Terraform required deep expertise in cloud architecture and configuration languages.
Post-AI: AI-powered tools such as ChatGPT, Claude, and Workik AI can generate Terraform scripts and other IaC templates from natural language, making it much easier to automate cloud deployments and simplify multi-cloud setups.
Impact: While AI tools can assist with the creation of basic templates, human oversight is still needed for security, best practices, and customization. The role of IaC engineers is shifting, with some tasks now easily handled by AI.
Ansible Playbooks:
Pre-AI: Writing Ansible playbooks to automate configurations and deployments was a specialized skill.
Post-AI: AI tools like IBM Watsonx and ChatGPT can generate Ansible playbooks based on natural language prompts, making automation tasks more accessible to a broader range of IT professionals.
Impact: AI-generated playbooks still need review and refinement by experienced engineers to ensure reliability and adherence to best practices, though the barrier to entry is now much lower.
PromQL Queries for Monitoring:
Pre-AI: Writing effective PromQL queries required specialized knowledge of Prometheus and its query language.
Post-AI: AI-powered tools like PromAssistant, Grafana’s query builder, and PromLens allow users to create PromQL queries from natural language input.
Impact: While AI simplifies query creation, a solid understanding of monitoring principles and context is still needed for accurate troubleshooting and actionable insights.
A Silver Lining: New Opportunities in the Age of AI
Despite the commoditization of some traditional DevOps skills and the decline of manual operations roles, the rise of AI has brought forth a wealth of new opportunities within the DevOps landscape. These emerging roles often require more complex and strategic thinking, leading to higher-paying positions for those who can adapt and acquire the necessary expertise. The evolution of DevOps is not about AI replacing humans entirely, but rather about augmenting human capabilities and creating demand for new specializations that can harness the power of AI.
Deep Dive into Emerging Opportunities
The DevOps job market is evolving rapidly, with new roles and responsibilities emerging to address the complexities of modern IT infrastructure and the integration of AI. For individuals seeking to enter or re-enter this field, focusing on these high-demand areas is key.
Skills to Design and Run Complex Pipelines
While AI can assist in generating basic CI/CD pipeline configurations, the ability to design and manage truly complex pipelines remains a critical skill. This involves understanding intricate workflows, managing dependencies between various stages, and tailoring pipelines to specific application needs and deployment scenarios. These pipelines often need to go beyond simply copying and pasting YAML from online resources, requiring a deeper understanding of the underlying technologies and how they integrate. AI can optimize CI/CD pipelines for intelligent automation, and complex workflows can be broken down into reusable components. However, the strategic design and implementation of these sophisticated pipelines for specific business requirements still necessitate human expertise and a comprehensive understanding of the Software Development Life Cycle (SDLC).
Ability to Make Fully Integrated Pipelines Keeping Security and On-Prem/Cloud Complexity
Modern IT infrastructure often spans both on-premises data centers and multiple cloud environments, creating significant complexity. Furthermore, security has become an increasingly paramount concern that needs to be integrated throughout the entire CI/CD pipeline. Designing fully integrated pipelines that can seamlessly manage this complexity while maintaining robust security and compliance is a highly sought-after skill. This requires expertise in both on-premises infrastructure and various cloud platforms, as well as a deep understanding of security best practices and tools. Hybrid AI infrastructure is also emerging as a common solution, further highlighting the need for professionals who can bridge these different environments. While AI can enhance DevOps security practices , the holistic understanding required to integrate security across diverse and complex environments remains a critical human skill.
Demand for People Who Can Actually Troubleshoot
Despite the advancements in AI-powered code generation and automation, the need for individuals with strong troubleshooting skills remains as vital as ever. AI can assist in identifying potential issues and even in root cause analysis, but it often falls short when it comes to understanding the context, complex logic, and real-world scenarios where code fails. AI lacks the deep understanding of context and the critical analysis capabilities that experienced DevOps engineers possess. The ability to effectively diagnose and resolve intricate problems in real-world systems, often under pressure, relies heavily on human intuition, problem-solving skills, and a deep understanding of how various systems interact. While AI can provide valuable assistance, it cannot fully replace the critical role of human troubleshooters.
Platform Engineers with Full Stack Knowledge
The role of a Platform Engineer is gaining prominence, requiring professionals with a comprehensive understanding of the entire technology stack, from the Linux kernel and administration to networking and programming. This skill set, which involves deep systems knowledge, is becoming increasingly crucial as DevOps evolves beyond basic automation. Many DevOps professionals who started their careers after the public cloud boom may lack this foundational infrastructure knowledge, having primarily worked with abstracted APIs . Platform Engineering, however, often involves situations where “nothing works,” demanding a low-level understanding that AI cannot currently replicate. Proficiency in Linux, networking, and programming languages is fundamental for building and maintaining the complex platforms that underpin modern software delivery.
Demand for SREs to Operate Observability Platforms Efficiently
Site Reliability Engineers (SREs) are in high demand, particularly those who possess expertise in operating observability platforms efficiently. With the increasing complexity and data volume of modern systems, the ability to effectively manage and optimize observability tools like Prometheus and Grafana is critical for ensuring system reliability. Moreover, as SaaS observability platforms can become expensive with higher data volumes, SREs with skills in cost optimization are especially valuable. SREs ensure system reliability through observability, and cost optimization is an increasingly important aspect of their role.
DevOps for AI — Demand for SRE/Platform Engineers to Operate AI Cloud and LLM Inferencing
The burgeoning field of “DevOps for AI” is creating a demand for SRE and Platform Engineers who can operate the specialized infrastructure required for AI workloads, including AI cloud platforms like those offered by Nvidia and AMD, as well as infrastructure for Large Language Model (LLM) inferencing. This involves managing GPU resources, ensuring security and compliance for AI services, and optimizing infrastructure for AI model training and deployment. This emerging niche requires a blend of traditional DevOps skills with an understanding of the unique demands of AI and machine learning.
Demand for LLMOps
The rapid growth of generative AI and Large Language Models (LLMs) has led to a specific demand for LLMOps engineers. LLMOps focuses on managing the entire lifecycle of LLMs, including data preparation, prompt engineering, fine-tuning, model deployment, monitoring, and addressing ethical considerations.This specialization differs from traditional MLOps by its emphasis on pre-trained models and techniques like prompt engineering. LLMOps engineers play a crucial role in ensuring the efficient, scalable, and reliable operation of these powerful AI models.
Demand for Kubernetes Operators
Beyond simply running managed Kubernetes services like EKS and AKS, there is a growing demand for expertise in developing and managing Kubernetes Operators. Kubernetes Operators extend the Kubernetes API to automate the management of complex, stateful applications. Building these Operators often requires skills in programming languages like Go, as well as a deep understanding of Kubernetes internals and the Operator pattern. The ability to automate the lifecycle management of applications on Kubernetes beyond the capabilities of SaaS offerings is a valuable and increasingly sought-after skill.
Table 1: Commoditization of DevOps Skills by AI
Skill | Pre-AI Criticality | AI Tools Impacting Skill | Impact on DevOps Job Market |
Pipeline Creation | High | GitHub Copilot, GitHub Actions | Reduced need for basic pipeline creation, broadened developer scope |
IaC Templates | High | Workik AI, ChatGPT, Claude, Gemini, Amazon Q, Firefly AI, Pulumi AI, env0 Cloud Compass | Easier and faster infrastructure provisioning, but requires review |
Ansible Playbooks | High | IBM watsonx Code Assistant, ChatGPT, other LLMs | Easier automation, but requires review and refinement |
PromQL Queries | Medium-High | PromAssistant, VizGenie, Grafana query builder, PromLens | Simplified query generation, democratized access to monitoring data |
WebOps, ClickOps | Medium | Every Co-pilot or AI Agent tools | As agentic workflow gets mature, there will be less manual/human interaction needed for UI based workflow. |
Table 2: Emerging High-Demand DevOps Roles in the Age of AI
Emerging Role | Key Skills Required |
Complex Pipeline Designer | Advanced CI/CD tools, workflow orchestration, integration expertise |
Secure Pipeline Engineer | Security best practices, DevSecOps, compliance, hybrid/multi-cloud management |
Troubleshooting Expert | Deep understanding of systems, debugging tools, problem-solving skills, scripting |
Platform Engineer | Linux, networking, programming, cloud platforms, automation tools |
SRE for Observability/AI | Observability platforms (Prometheus, Grafana), cost management, AI/ML basics |
LLMOps Engineer | Prompt engineering, fine-tuning, model deployment, monitoring, NLP frameworks |
Kubernetes Operator Expert | Go, Ansible, or Helm, Kubernetes internals, Operator pattern |
Navigating the Shift: Skills for the Future DevOps Engineer
For individuals looking to thrive in this evolving DevOps landscape, several key strategies can be adopted. First, building a strong foundation in core DevOps principles and practices remains essential, regardless of the tools being used. Understanding concepts like continuous integration, continuous delivery, infrastructure as code, and monitoring will provide a solid base upon which to build more specialized skills. Second, embracing continuous learning is paramount. The technology landscape is constantly changing, and staying updated with the latest trends in AI, cloud computing, and DevOps tools is crucial for remaining relevant. Third, identifying one or more of the emerging high-demand areas, such as those outlined above, and focusing on developing expertise in those areas can significantly enhance job prospects. Fourth, gaining practical experience through personal projects, contributing to open-source initiatives, or seeking internships and entry-level roles that offer exposure to these new technologies can be invaluable. Finally, networking with industry professionals, attending conferences and meetups, and actively engaging with the DevOps community can provide valuable insights and connections that can aid in career advancement.
Conclusion: Embracing the Evolution of DevOps
The impact of AI on the DevOps job market is undeniable. While some traditional skills have been commoditized, leading to a shift in demand, this evolution has also created a wealth of new and exciting opportunities. The future of DevOps lies in embracing AI as a powerful tool and focusing on developing the advanced skills required to design and manage complex, secure, and intelligent systems. For those seeking to enter or navigate this dynamic field, the key is to remain adaptable, continuously learn, and focus on the emerging areas where human expertise, augmented by AI, is most valued. The DevOps field has always been about embracing change and driving innovation, and this latest transformation is no exception. By focusing on the right skills and adopting a proactive mindset, aspiring DevOps professionals can not only find their place in this evolving landscape but also thrive in the age of AI.
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