Aadya Madankar
My Story
I entered AI engineering in early 2022, navigating a field demanding technical precision and creativity. Through consistent experimentation, my machine learning curiosity at Priyadarshini College evolved into expertise in generative AI and prompt engineering.
I came into AI with strong foundations in machine learning and NLP, but the open-source community truly shaped me. Through contributing to projects and solving complex problems alongside talented developers, I discovered my engineering voice—one that bridges academic knowledge with real-world solutions.
It was the late-night debugging sessions, iterating on architectures, and learning from failures that forged my approach to engineering:
practical, persistent, and always learning.
I'm curious about where this path leads and the people who'll shape the journey.
Let’s talk.
Why I choose
Engineering ?
Engineering is strategic work,
where exactness meets ingenuity.
Every project teaches something—failed experiments sharpen debugging skills,performance bottlenecks refine architectural thinking, and production issues expose assumptions worth challenging.
Through iteration, patterns emerge—where to optimize, what to abstract, when to rebuild—and engineering becomes about working smart, focusing effort where it creates the most impact.
Engineering demands both macro vision and micro precision—understanding user needs while debugging edge cases, architecting scalable systems while writing testable code, balancing innovation with maintainability.
Working independently creates clear ownership with fewer handoffs. This autonomy enables focus on building reliable solutions, not managing complexity.
Engineering thrives on experimentation—exploring new frameworks, testing unconventional approaches, questioning established patterns—but always with intent, not just for novelty's sake.
The challenge isn't finding inspiration—it's filtering what deserves focus. Great engineers know when to explore new technologies and when proven solutions work best.
So why work
with Me?
I care about
the details.
Every project begins with understanding requirements—what you need built, what constraints exist, and what success looks like. No generic templates. I work with you to ensure the solution fits your specific problem, whether it's building RAG systems, optimizing AI models, or deploying production-ready applications.
You'll see progress iteratively, not in one final reveal. Continuous updates mean we stay aligned throughout development.
My expertise spans Machine Learning, Deep Learning, NLP, Data Science, and Computer Vision using TensorFlow, PyTorch, scikit-learn, and Keras. I specialize in Generative AI, Prompt Engineering, and RAG systems with LangChain, LangGraph, LlamaIndex, and multimodal models like Gemini, GPT-4o, and Claude.
I work across frontend and backend development, manage databases, and deploy applications to production.
I believe in creating AI that empowers individuals but with human values at the centre, systems that are transparent, accountable and systems that are made with the end user in mind.
Great AI engineering is knowing when to apply the technology and when human judgement can never be replaced by algorithms so that the systems that we create now can make humanity better instead of undermining the ethical views that shape us as human beings.
