AI Engineer | Full-Stack Developer | Research Fellow

I architect intelligent systems where AI research meets production reality. My work spans medical AI, multi-agent systems, and data-intensive applications—built to solve real problems, not just proof-of-concepts.

I'm that rare engineer who taught physics to teenagers before building neural networks—and that journey matters. Six years in classrooms taught me to explain complex ideas simply, debug human confusion as rigorously as code bugs, and design systems people actually want to use.

Now I build at the intersection of AI research and production engineering:

• 🏥 Contributing to medical AI research and clinical data applications
• 📊 Engineering data pipelines and analytics platforms
• 🎨 Crafting polished, pixel-perfect web experiences

Technical expertise:

AI/ML          → PyTorch, Transformers, Multi-Agent Frameworks, Amazon Bedrock
Databases      → Neo4j, PostgreSQL, MongoDB, Snowflake, Vector Databases
Full-Stack     → React, Next.js, TypeScript, Node.js, FastAPI, Flask
Cloud/DevOps   → Docker, Kubernetes, Azure, CI/CD pipelines
Specialties    → Knowledge Graphs, NLP, Low-Resource Languages

I bridge worlds most engineers don't
From teaching logic gates to high schoolers to implementing HIPAA-compliant medical AI—I speak both human and machine fluently. This makes me effective at research-to-engineering translation, explaining AI to stakeholders, and building tools actual humans can use.

I finish what I start
Not interested in half-baked demos. I build complete systems: authentication, error handling, monitoring, documentation—the unglamorous stuff that separates toys from tools.

I solve problems end-to-end
Need NLP for low-resource languages? I'll collect the dataset, train the model, build the API, and deploy it. Need analytics? I'll design the schema, build the ETL, create the dashboard, and integrate AI insights. No hand-offs, no excuses.

Pragmatic AI: Not everything needs a transformer. Sometimes rule-based systems, sometimes hybrid approaches, sometimes just good SQL. I choose based on constraints, not hype.

Production-First: Research is exciting, but I'm measured by deployed systems. Security, scalability, maintainability—non-negotiable from day one.

Explainable by Default: If I can't explain how my model works to a domain expert, I haven't finished building it. Especially true in healthcare and critical systems.

Code is Communication: Write for the next developer (usually future me). Clean, documented, testable. No clever tricks that require a PhD to understand.

I'm interested in challenging problems where AI research meets real-world constraints—especially in healthcare, knowledge systems, and low-resource language applications.

Open to:

• AI research collaborations (medical AI, multi-agent systems)
• Full-stack AI product development
• Technical consulting for ML/NLP projects

📧 [email protected]
💼 linkedin.com/in/aayush518
🔗 github.com/Aayush518