Career-Ops founder builds AI job filter from 700 listings—and lands head AI role
AI job – A Seville AI leader created Career-Ops to sift hundreds of job listings and match his profile—then used the project as a portfolio to earn a head of AI position.
Job searching is often treated like a waiting game. Santiago Fernández de Valderrama Aparicio turned it into a build-and-measure project instead—and that choice ended up changing his career.
His tool. Career-Ops. was designed to take a messy pool of listings and apply a structured filter based on a candidate’s experience and interests.. He says the starting point was simple: after founding a retail phone repair business 16 years ago. he realized the most satisfying part wasn’t just running the operation—it was using software to automate work and create better value for customers.. When he decided to shift into artificial intelligence. he didn’t want to only learn the technology; he wanted to prove he could use it.
That mindset quickly ran into a reality many job seekers recognize: finding a job is its own full-time task.. Santiago began building Career-Ops as a way to handle the volume problem—moving faster than manual review while still matching the roles that fit his background.. Over roughly six weeks. he used AI coding workflows to assemble the system and then refined it when something didn’t look right. using his technical background to sanity-check results.. When the tool was ready. he says it evaluated more than 740 job listings and recommended that he apply to 66 open positions.
Those numbers mattered, not as trophies, but as feedback.. From those recommendations, he landed 12 interviews.. Eventually, he accepted an offer as head of AI in April.. The straight line—from tool-building to a leadership role—wasn’t accidental.. Santiago frames Career-Ops as “proof-of-work. ” arguing that in the AI era. your strongest résumé is often evidence that you can ship useful systems.
Why “filtering listings” is becoming a competitive advantage
Career-Ops aims at a specific pain point: job platforms can be overwhelming, and matching isn’t just about keywords.. In practice. a good filter has to understand intent—what kind of work a person actually wants—and translate that into actionable recommendations.. Santiago’s example of a software engineer wanting to build with AI illustrates the approach: the user communicates preferences. and the tool returns listings based on both job titles and the substance of descriptions.
This matters because job searching increasingly depends on scale.. If a candidate applies to the wrong roles, time gets wasted; if they apply too narrowly, they miss opportunities.. An AI-assisted filter can help rebalance that tradeoff by widening candidate discovery while still keeping relevance high.
Just as important, Santiago didn’t keep Career-Ops locked behind a paywall.. He released it publicly and later concluded that monetizing it as a job-search “need” didn’t feel right.. The public release did more than help users—it also helped him build credibility.. When he posted on a community forum. the response wasn’t limited to praise; people asked for the code. which he took as a signal to make the project open source.
Building in public—and learning from users
Open source changes the feedback loop.. It invites edge cases. new use patterns. and the kinds of real-world friction you only discover when others try to run the tool for themselves.. Santiago says the primary response he saw was gratitude. but the second was curiosity with a practical question: how can you use it too?
That pushed him toward accessibility improvements.. The setup is described as a bit technical, so he created a Discord community to support implementation and encourage collaboration.. According to his account, the community grew quickly—over 1,000 members within a week of launching.. In that space. people don’t just consume the system; they help iterate it. which can be a powerful way to refine how recommendations are generated.
There’s a business lesson here for anyone watching the AI tools market.. Many AI products stall at the “demo” stage.. Santiago’s approach treated deployment and onboarding as part of the work.. That’s a mindset more common in engineering than in consumer software. but it’s exactly what helps tools survive contact with users.
What Career-Ops suggests about the next job-search platforms
Santiago’s broader vision goes beyond personal efficiency.. He imagines job seekers using AI to filter companies. but also potentially uploading CVs and supporting materials—turning the candidate profile into a living dataset.. On the other side, companies could publish roles, creating something closer to a marketplace.. Even if the final shape evolves. the direction reflects a shift in how matching may work: less “search and sort. ” more “recommend and verify. ” with AI in the middle.
His “ultimate résumé booster” framing is also telling.. He argues that building a tool that others can use creates stronger signal than simply describing skills.. For candidates. side projects have always been part of tech culture. but AI lowers the barrier—meaning more people can ship prototypes that feel closer to real products.. The risk is that tools can become generic; the opportunity is that serious candidates can differentiate by focusing on one painful workflow and making it measurably better.
For employers, the trend could reshape hiring too.. When candidates can demonstrate that they built working systems—especially systems that solve a known real problem—evaluation becomes less abstract.. Santiago’s story is essentially a case study in how a job seeker used product development as an employment strategy.
As he continues to make Career-Ops more accessible. the key question for the future is whether the tool stays a personal portfolio project—or grows into a broader platform that supports many job seekers.. If the open-source community can keep improving setup and recommendation quality. Career-Ops may become one of the cleaner examples of where AI can deliver value in the most competitive part of the labor market: being seen. quickly. by the right opportunities.