Full Stack Developer
Software Engineer with 3+ years of experience building production applications and scalable infrastructure. I specialize in AI/ML integration, full-stack development, and turning innovative ideas into production-ready solutions. Currently pursuing a Doctorate in Business Administration with a focus on Business Intelligence & Data Analytics.
I'm seeking challenging opportunities in AI product development where I can leverage my technical expertise while expanding into cutting-edge AI technologies. I'm particularly drawn to startups working on climate tech, sustainability, and responsible AI applications.
Authorized to work full-time via CPT (Curricular Practical Training). Available for remote, hybrid, or onsite positions in the Bay Area.
Seeking opportunities with skilled worker visa sponsorship. Open to AI/ML and full-stack engineering roles in London startups.
Hands-on experience with LLM APIs, agent systems, and production AI applications. Built real-time analytics platforms with intelligent recommendations.
End-to-end development from React frontends to Python backends, with expertise in scalable infrastructure and real-time data processing.
Proven track record in fast-paced startup environments, from prototype to production deployment, with focus on rapid iteration and user feedback.
Currently conducting doctoral research on predictive analytics for financial systems, combining academic rigor with practical applications.
Concentration: Business Intelligence & Data Analytics
Format: Online
Status: Currently in final year, expected graduation August 2026
Concentration: Database Management & Business Intelligence
GPA: 3.60
CGPA: 8.9
Topic: Quantitative analysis of reputational contagion effects on smaller banks when larger banks collapse
Methodology: Advanced machine learning models for multimodal data analysis with high precision, establishing foundation for predictive analysis in financial systems
Applications: Risk assessment, financial stability prediction, systemic risk modeling
Predictive analytics for financial stability, risk assessment models, and systemic risk analysis using machine learning approaches.
Fair usage of AI systems, responsible AI development, and regulatory frameworks for AI applications in business contexts.
Data-driven approaches to sustainability measurement, environmental impact assessment, and climate technology optimization.
Advanced analytics for business decision-making, real-time data processing for operational insights, and predictive business modeling.