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About
Backend engineer who built and evolved Python/FastAPI services (including AWS-deployed ML prediction APIs) for real-time profitability and risk insights at TenXengage. Emphasizes pragmatic architecture, strong validation/observability, and secure access controls (RBAC + row-level filtering), and has led safe migrations via parallel runs and incremental rollouts; reports ~20% forecasting accuracy improvement.
Experience
Data ScientistTenXengage Inc
Data ScientistGeni Community Pvt Ltd
Education
University of North Carolina, Charlottemaster, Computer Science (2025)
Key Strengths
Designed scalable Python backend for real-time profitability/risk insights
Improved forecasting accuracy by ~20% via production ML inference system
Pragmatic architecture tradeoffs (simplicity vs microservices; model complexity vs latency)
Strong focus on data quality, edge cases, and robust failure handling for noisy/incomplete inputs
Built reliable FastAPI services with strict validation and standardized errors to reduce production failures
Security-minded API implementation (token auth, RBAC, row-level filtering, least-privilege access)
Led low-risk refactor/migration using parallel run, output reconciliation, incremental traffic shift, and rollback
Reference Highlights
Strongly Recommended
Strong technical knowledge
End-to-end ownership from design through production
Takes responsibility for reliability and data integrity
Strong architectural judgment with long-term impact
Applies security best practices
Balances simplicity with scalability
Designs reliable and maintainable systems for small teams
Makes decisions based on real usage and business needs
Effective collaboration with frontend teams
Proactive in understanding frontend workflows
Responsive to integration issues
Comfortable in ambiguity and early-stage environments
Moves systems into production quickly
Defines scope and makes thoughtful tradeoffs
Adapts as requirements evolve
Focus on long-term system health
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