Building AI that keeps people in charge.
I am Jignesh Trivedi, an engineering leader and software architect. For 16+ years I have built reliable systems and grown engineering teams, most recently in healthcare technology, where software has to be right. Now I am bringing that discipline to human-first AI: systems that amplify human judgment instead of replacing it.
On its own, the problem is bigger than any one person.
Software engineering, then AI, then products.
The path I have been on, and the one I am building toward.
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Software engineering
Sixteen years of building systems hands-on, from code to architecture that has to stay reliable when real people depend on it.
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AI
Treating AI as a core part of how software is built, not a bolt-on feature: agents, persistent context, and AI-native architecture.
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Products
Turning both into products that fit how people actually work, so the technology fades into the background and the leverage stays.
Why people get in touch
Pick the one that sounds like you.
An engineering leader who still ships code.
I lead and grow engineering teams while staying hands-on in architecture, design reviews, and hard technical problems.
- Grew developers into senior and mentoring roles through hands-on coaching.
- Designed and reviewed scalable, secure systems in healthcare, where reliability is not optional.
- Raised engineering standards through testing, code review, and sustainable delivery.
- Brought AI-assisted engineering into daily team practice without lowering the bar on quality.
Architecture judgment for AI-native products.
If you are building or backing AI-native software, I would like to compare notes on where agents genuinely help, how to architect for them, and how to build teams that ship them.
- A hands-on view of how engineering principles change in an AI-native world.
- Experience making build-vs-buy, modernization, and system retirement decisions.
- A clear, human-first point of view on what AI products should do for people.
Swap ideas, not pitches.
I am experimenting with AI-native systems and writing about what I learn. If you are thinking about the same questions, I would like to hear from you.
- Persistent context layers and world models.
- Agents that amplify human judgment rather than replace it.
- Software architecture for AI-enabled systems.
What I believe about AI
Not AI for the sake of AI, but AI that gives people more power.
- Augment, don’t replace.
- AI should help people think, create, learn, and decide more effectively, without taking away their agency.
- Fit how people work.
- The best AI experiences fit naturally into existing work. People should not have to reshape themselves around the tool.
- Fundamentals still matter.
- Reliability, simplicity, and maintainability don’t expire in an AI-native world. They evolve, and they matter more.
Let's talk.
Hiring for an engineering or AI role, building something AI-native, or just thinking about the same questions? Email is the best way to reach me.