Hybrid engineer in training, focused on building scalable software systems that are data-driven and AI-assisted. Currently pursuing M.Tech in Information Technology (Data Analytics) with active research in AI/ML.
- Designing backend systems with strong CS fundamentals
- Integrating AI/ML into real-world software workflows
- Writing production-oriented code with clarity, performance, and security in mind
- Solving problems where data and engineering meet
- Programming: C++, Python, JavaScript
- Software Engineering: DSA, OOP, OS, DBMS, Computer Networks
- Backend & Web: MERN stack, REST APIs
- AI & Data: NumPy, Pandas, ML fundamentals, NLP exposure
- DevOps & Cloud: Linux, Docker, AWS (EC2, S3 basics)
- Data Thinking: Analytics, pipelines, model evaluation, decision support
- Machine Learning & Artificial Intelligence
- Big Data Analytics
- Natural Language Processing
- Advanced Data Structures & Algorithms
- Data Science & Business Intelligence
- Social Network Analysis
- AI/ML-focused research with emphasis on practical applicability
- Interested in model reliability, data quality, and system-level integration
- Treat AI as a system component, not a black box
- Focus on data preparation, evaluation, and controlled deployment
- Prefer human-in-the-loop and explainable approaches where applicable
- Fundamentals that outlast tools and frameworks
- Clean system design over quick hacks
- Learning that compounds over time
Targeting hybrid engineering roles where I can work on software systems that are AI-integrated, data-driven, and built with strong engineering discipline.