Open to opportunities

Nehan Teja Anagam

@nehantejaanagam

Message

Software engineer building production full-stack systems and AI-enabled platforms with measurable impact.

United States

Message

What I'm looking for

I’m looking for a team where I can build reliable full-stack/back-end products and real-world AI (RAG, LLM integrations) with strong engineering discipline—testing, monitoring, and CI/CD—so I can deliver measurable performance and uptime.

I’m a software engineer focused on shipping production-grade backend and full-stack systems, plus practical AI experiences. In my recent intern role at Triage Partners, I built C#/.NET backend services processing 10,000+ records/day with zero production incidents, and I improved performance by optimizing SQL Server queries and redesigning stored procedures—cutting latency by 30–40%.

Across roles, I’ve automated high-impact pipelines (including a validation pipeline that eliminated 50% of manual reconciliation effort) and owned features end-to-end from requirements through CI/CD deployment with strong testing practices (85%+ xUnit coverage). In projects like DocMind AI, I integrated Claude-based RAG with real-time streaming and delivered sub-100ms latency at 99%+ uptime, while other work (e.g., PlateWise) combined product iteration with scalable APIs and database performance for low-latency search.

Experience

Work history, roles, and key accomplishments

Software Engineer Intern

Triage Partners, LLC

May 2025 - Aug 2025 (3 months)

Built C#/.NET backend services processing 10,000+ records/day across enterprise inventory and asset-tracking workflows with zero production incidents. Optimized SQL Server performance (30–40% lower latency) and automated data validation to eliminate 50% of manual reconciliation effort, supported by 85%+ xUnit coverage and structured CI/CD releases.

C++.NET ASP.NET Core SQL Server Query Optimization Stored Procedures Data Validation xUnit CI CD

Software Engineer

Alight Solutions

Jul 2022 - Jul 2024 (2 years)

Developed and maintained Python backend services for high-volume transactional workflows across enterprise production environments. Built rule-based data processing pipelines cutting manual operational effort by ~80%, improved processing accuracy, and used SQL validation and log-based debugging to accelerate production stabilization.