Resume builders in the age of AI: How to future-proof your testing career

Just a few years ago, AI’s effect on software development was debatable — would it be as transformative as everyone predicted? But its impact is undeniable. In a 2024 survey, a majority of developers reported using AI in development, a sharp increase from the year before.

In a short period of time, developers adapted, integrating agentic AI into their daily operations to boost productivity. Quality assurance teams are not far behind. AI-powered agents that create, edit, and run tests are set to transform QA workflows. Quality engineers, once preoccupied with manual tasks that AI agents can now perform in seconds, must develop an AI-driven skillset for the future.

But where to start?

As calls for QA upskilling grow, it can be difficult to know how to future-proof a testing career. Adam Satterfield, VP of Engineering for Quality and Delivery at Qualifacts, has 20 years of experience leading quality, software development, cloud operations, DevOps, and security teams. As a recognized thought leader in AI, he suggests that quality engineers — both new and senior — approach upskilling as they would most challenges: start with the basics, then build from there.

AI will change — not eliminate — the QE role

After decades of experience, Satterfield recognizes that the quality industry is at a critical turning point. He urges testers to start curating an AI-centered resume now — not because he believes AI will replace manual testers, but because it is going to reshape the job and its requirements.

“It’s going to change things,” he said. As a tester, Satterfield said you want to be sure to have an answer when potential employers ask — and they will ask — “Hey, what is the latest project you worked on that had AI in it?”

For testers who don’t yet have an answer, he suggests first becoming familiar with basic generative AI principles and then advancing to more complex concepts.

First things first: Get familiar with generative AI

Satterfield said testers should be most familiar with two generative AI concepts: prompt engineering and context engineering. Prompt engineering is the foundational (and probably somewhat familiar) AI skill, easily summed up by Satterfield: Learn how to speak to AI, “how to get information into it and out of it.” Testers should understand how to craft prompts for a transformer-based LLM to get the most relevant and accurate responses.

Testers should then master context engineering, which involves chaining direct, detailed prompts together to sharpen the LLM’s focus. As Adam notes, this could include giving the LLM both a persona and a task, like: “You are an expert tester in the financial technologies space … and you’ve been given the task to research how to test security for the PIN on a credit card.” And then, after the LLM absorbs this context, ask it, “Based on that, what are the top 10 test cases you would look at first?” 

Assigning personas and linking prompts gives the LLM a deeper understanding of the task at hand. “It drives the LLM to behave,” Satterfield said, weeding out generic answers. This is a critical skill for testers as AI agents that respond to natural language prompts become more common in QA organizations.

Take it to the next level: AI engineering

The next level of upskilling — the one that equips QE professionals to bring actionable change to their team — is AI engineering.

AI engineering is about deploying AI into an organization’s ecosystem, integrating AI into products or systems to solve real-world problems. This could include a custom project, like using AI to build an application that converts one coding language to another to better understand its logic. Or it could be as simple as investigating the AI features that are being built into the tools you already use, identifying valuable ways to use them, and adjusting processes accordingly.

To become familiar with AI engineering, Satterfield stresses that testers don’t need to become professional AI engineers or coders. Even without basic coding skills, junior testers can use AI to streamline time-consuming admin tasks or even drive enterprise-wide change. Testers just need to become familiar with the tools, either through their own organization or independently, and understand how they can quickly translate baseline AI experience into real-world results.

For those who want to dive deeper

A firm grasp on basic generative AI and AI engineering concepts is sufficient to prepare for the future, Satterfield said. “If you are a tester, and you even just kind of stop there, I think that pretty much sets you up for pretty good success.”

Testers only need to gain in-depth machine learning expertise if they wish to eventually test AI systems or become involved with a fully AI-based product. In that case, QE professionals should gain more hands-on experience, improving coding capabilities and studying agentic architecture and frameworks like Model Context Protocol (MCP) and Retrieval-Augmented Generation (RAG).

Satterfield notes that more companies are now creating AI Centers of Excellence and innovation teams that strategize and create prototypes for new AI products and agents. An AI agent, as Satterfield defines it, is just a “code-wrapper around an LLM.” This code dictates what an LLM should do and specialize in. These innovation teams will need QA professionals, versed in AI principles, to test that code. If a tester wants to switch to a more AI-focused career, this would be a route worth considering.

Focus on the value, not the skill

Satterfield said that even he, a classic, legacy QA professional, has had to work hard to gain more AI expertise. During this transition, he was guided by the advice of a peer: “One of the things she said was, ‘Everyone is able now to talk the language … they’re throwing out terms –LLMs and transformers and generative AI and agentic AI, and stuff like that. But what’s different is your ability to drive that into a solution, into a product, or into a prototype.’”

In this season of upheaval, a new skill or recently acquired knowledge is not enough to future-proof your career. What will truly differentiate a tester is how they translate their AI-skillset into concrete value for an organization.

To prove your AI chops, QE professionals should be asking themselves and their teams: “What are the painful things you have to do every single day, and is it something that AI can help you with?” Whether it’s analyzing lengthy requirements documents, identifying test coverage gaps, or fixing flaky tests, chances are, there’s an agent for that. Being able to solve these problems will take you from a self-professed AI wizard to a known QE + AI expert within your organization.

Want to learn more?

Watch our webinar, Your QA job as you know it is changing. How can you adapt? for an in-depth discussion about AI’s effect on the QE role. To see agentic AI in action, check out a demo of Tricentis Agentic Test Automation.