Here's a question that keeps ambitious engineers up at night: "What should I learn next?"
You're a backend developer wondering if you should pivot to machine learning. Or a frontend engineer curious about full-stack development. Or a data analyst eyeing data engineering roles. Everyone tells you to "follow your passion" or "learn what interests you" - but what if you want to make strategic decisions based on actual market reality?
That's the problem we built Ingrid to solve.
Most career advice falls into two categories: either it's generic ("learn Python, it's popular!") or it's anecdotal ("I switched from X to Y and here's my story"). What's missing is systematic, data-driven guidance that shows you:
We analyzed over 20,000 real job postings across 43+ distinct software engineering roles. Not job titles - actual roles with specific skill requirements. We broke down the entire software engineering landscape into clear tracks: AI/ML, Application Development, Distributed Systems, DevOps, QA, Systems Engineering, Specialized Backend, and Leadership.
Then we did something different: we mapped the skill overlaps between every single role.
Want to transition from Data Engineering to MLOps? We'll show you that you already have 80%+ of the technical stack - Python, Docker/Kubernetes, cloud platforms, workflow orchestration. We'll tell you exactly what's new: ML frameworks, model serving, experiment tracking. No guesswork.
Traditional career advice treats every transition like starting from scratch. But that's not how expertise actually works. If you're a backend developer who knows distributed systems, you're not a "beginner" learning DevOps - you already understand half the mental models.
Ingrid quantifies this. We show you which roles share your existing competencies, which skills transfer directly, and where the learning curve is steepest. It's like having a map of the entire software engineering landscape with your current position marked clearly.
This isn't about chasing trends or following what's "hot" right now. It's about understanding the market structure so you can make informed choices about your learning path.
Whether you want to:
Ingrid gives you the data to decide strategically, not emotionally.
We've organized software engineering into tracks that reflect how skills actually cluster in the real world:
AI/ML Track - From data analytics to machine learning engineering, these roles form a natural progression based on mathematical and statistical foundations.
Application Development Track - Frontend, backend, mobile - building user-facing applications with shared concerns around state management, APIs, and user experience.
Distributed Systems Track - Microservices, streaming systems, asynchronous messaging - the architecture of systems that scale.
DevOps Track - Infrastructure, cloud, security, observability - keeping systems running reliably at scale.
QA Track - Testing isn't just one skill - it's a specialization with its own depth across frontend, backend, mobile, and performance domains.
Systems Engineering Track - Embedded systems and systems software for those who work closer to the hardware.
Specialized Backend Track - Fintech, e-commerce, gaming, IoT, search - domains where business logic complexity is the challenge.
Leadership Track - Technical leadership and project management for those ready to scale their impact through others.
Ingrid wasn't built by career coaches or HR consultants. It was built by someone who's lived the journey.
I'm a communication systems specialist with a BTech from IIT Madras, and I've spent about 15 years in the software industry. I also studied product management at Northwestern Kellogg to better understand how to build solutions that actually solve real problems. I've worked at places like LinkedIn, which gave me a pretty good view of how the software engineering landscape actually operates - not the polished LinkedIn profiles version, but the messy reality of how engineers actually build careers.
That experience showed me something: there's a huge gap between how people make career decisions (usually based on whatever they hear from friends or see trending on Twitter) and the actual structured data that exists about what skills matter and how roles connect.
Ingrid is my answer to that gap. It's the career intelligence tool I wish existed when I was trying to figure out my own path.
Job titles lie. A "Senior Software Engineer" at one company might be doing frontend React work, while at another they're designing distributed systems. Ingrid cuts through the title confusion and shows you what skills actually define each specialization.
We're not here to tell you what you "should" do. We're here to show you what's actually possible from where you are right now, backed by real market data from thousands of job postings.
Because in the end, the best career decisions aren't made by following someone else's path - they're made by understanding the landscape well enough to chart your own.
