I design and build end-to-end intelligent systems—from data pipelines and backend architecture to applied machine learning models and research-backed prototypes.

My work sits at the intersection of:

• Software engineering
• Applied machine learning
• Systems thinking
• Research-to-production workflows

I optimize for long-term capability, not short-term visibility.

In practice, I:

• Build production-grade backend systems and APIs
• Design ML pipelines from raw data → inference → application
• Apply statistical learning to real-world, noisy domains (especially healthcare)
• Translate research ideas into deployable, constrained systems

I routinely bridge roles that are often siloed:

System engineer × ML engineer × data scientist

Backend & Systems

• Node.js, Express
• MongoDB
• Python: Django, FastAPI
• API-first design, authentication, scalability considerations

Data & Machine Learning

• Python, NumPy, Pandas, SciPy
• Scikit-learn
• Regression (linear & non-linear), classification
• Feature engineering, validation, model evaluation

I work from first principles:

• Data before models
• Systems before frameworks
• Understanding before optimization
• Research before scaling

This keeps my work adaptable across domains and resilient to tooling churn.

A research-driven system focused on:

• Blood glucose prediction
• Non-linear statistical modeling
• Reliable inference under noisy, real-world data

Initial experimental validations show ~X–Y% improvement over baseline statistical predictors under controlled conditions. The system is architected to evolve from experimentation to clinically relevant deployment, not as a demo artifact.

I approach AI as a scientific discipline, not a feature set.

Current interests include:

• Predictive healthcare analytics
• Applied statistical learning
• Model reliability and interpretability
• Translating mathematical assumptions into operational systems

My long-term trajectory includes PhD-level research in AI / Computational Intelligence, with emphasis on original contribution and practical relevance.

In parallel with ML systems, I explore infrastructure-level problems, including feasibility work on improving digital connectivity in Nepal. This reflects the same systems mindset: constraints, incentives, reliability, and long-term impact.

• AI / ML engineering roles
• Research collaborations
• System design problems with real constraints
• Work that compounds over years, not quarters

I’m not optimizing for visibility. I’m optimizing for depth, leverage, and durability.

If you value:

• Systems thinking
• Research-backed engineering
• Quiet execution over loud claims