How does DevFound's AI job matching work?

DevFound's AI Copilot ingests your profile, goals, and live job data to deliver curated matches in seconds. Every match includes a resume variant, suggested referrals, and interview prep so you can act immediately. The more feedback you provide, the sharper the Copilot becomes.

Is AI job searching better than traditional methods?

AI-led job searches shrink the hours spent sifting through boards and formatting resumes. DevFound pairs automation with your personal outreach, so you reserve energy for interviews and negotiation. Traditional networking still matters, but AI gives you a lift before you even send a message.

What skills are needed for AI and ML jobs?

Modern AI roles expect comfort with production-grade code, data fluency, and practical ML tooling. The strongest candidates pair deep technical chops with storytelling—translating model impact to product, GTM, and exec partners. Continuous learning keeps you ahead as stacks evolve.

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DevFound rewards active seekers. Keep your profile fresh, respond to match quality prompts, and enable alerts so you never miss a role. The AI prioritizes companies and teams that align with your feedback, accelerating both introductions and interview invites.

What locations have the most AI job opportunities?

High-density tech hubs continue to host the deepest AI talent pools, yet distributed teams are catching up fast. Use DevFound filters to hone in on onsite, hybrid, or fully remote roles and watch openings expand across time zones.

Can DevFound's AI help with remote AI jobs?

DevFound aggregates thousands of remote AI openings and flags the nuances—core hours, async culture, and visa needs—up front. The Copilot also recommends how to position your distributed work experience so hiring managers know you can thrive on a remote team.

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Agile Meets Product Meets Software Craft in AI-Enabled Delivery

20 May Online

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About this event

AI tools are rapidly changing the software development lifecycle, but teams rarely get the full benefit by simply dropping new tools into existing ways of working. The bigger shift is operational: teams need to adapt how they plan, learn, experiment, and collaborate so they can make effective use of AI in day-to-day delivery.

This session explores how Agile workflows can evolve to support faster feedback loops, smaller and more testable units of work, clearer cross-functional collaboration, and more intentional experimentation as teams adopt AI-assisted development. The focus is on practical process changes that help teams learn quickly without losing alignment, quality, or delivery discipline.

Nick and John will share a joint perspective from Agile coaching and software engineering on how teams can refine their workflow to better support AI-enabled delivery: from how work is framed, to how experiments are run, to how teams inspect and improve their process as adoption matures. Attendees will leave with practical ideas for evolving their team’s workflow so AI becomes part of a sustainable delivery practice rather than a disconnected layer on top of it.

At the end, the session will briefly point to En Dash’s open-source n-dx toolkit as one example of this thinking in practice: a developer workflow that connects analysis, planning, and execution, with straightforward adoption for teams already using Claude Code or Codex. n-dx is designed as a CLI toolkit built around SourceVision, Rex, and Hench, and supports both Claude and Codex through its unified configuration and orchestration flow.

SPEAKERS:
John Halberstadt and Nick Daniel

Nick and John are key leaders at En Dash, bringing complementary, comprehensive expertise across product, technology, engineering, and enterprise transformation.

Nick, a co-founder and digital product leader, possesses over a decade of hands-on experience guiding product delivery, engineering execution, and Agile transformation. With a background encompassing full-stack engineering and various client-facing roles—including Tech Lead, Product Owner, and UX/UI Designer—Nick excels at connecting technical depth with strong product judgment to improve end-user experiences.

John, Head of Consulting Operations, is a strategic product, technology, and AI leader specializing in enterprise transformation, product development, and engineering modernization for complex organizations.
John’s experience, which includes prior roles as a CIO/CTO/VP of IT in Financial Services and Insurance and leading product and UX for AI/NLP platforms, allows him to operate at executive and hands-on levels, advising leadership while shaping product strategy and delivery models.

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