Nakul already has a relationship with Reval, so a warm intro from us gets a much better response than cold outreach.
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About
Software engineer with JPMorgan Chase experience building a real-time operations console backend on Spring Boot/Kafka/Kubernetes and resolving peak-load latency through profiling, indexing, caching, and async processing. Also built and owned an AI-driven digital-archives metadata pipeline during a master’s at UNT using OCR + LLaMA-based prompting with validation, near-human accuracy, and human-in-the-loop guardrails.
Experience
Software Engineer InternJ.P. Morgan Chase & Co
Software EngineerCSS Info Systems
Software Engineer InternWipro
Education
University of North Texasmaster, Information Science (2025)
Malla Reddy Engineering Collegebachelor, Information Technology (2023)
Key Strengths
Built real-time backend system (Spring Boot/Kafka/Kubernetes) supporting 10,000+ transactions/users
Debugged and reduced peak-load latency via profiling, query/index optimization, caching, and async processing
End-to-end ownership of AI metadata automation pipeline (OCR + LLaMA prompting + validation) achieving near-human accuracy
Designed reliable multi-step workflow with checkpoints/state machine, retries with exponential backoff, and escalation to manual review
Strong production engineering practices in Python (typing/mypy, asyncio/Celery, pytest, CI, logging/monitoring)
Improved slow relational query performance by analyzing query plans and adding targeted indexes (seconds to milliseconds)
Reference Highlights
Strongly Recommended
Highly effective backend engineer (rated 9/10)
Strong proficiency in ASP.NET Web API
Builds well-structured, reusable API controllers and service logic
Designed and implemented core API integration layer between Angular and ASP.NET
Strong request/response and error-handling practices
Improved product discovery and user interaction via LLM assistant
Reduced navigation friction and time-to-find relevant products
Disciplined workflow design with clear planning/execution/verification stages
Uses structured data contracts to improve reliability
Writes clean, modular Python with separation of concerns
Strong debugging and issue isolation skills
Thoughtful schema design with clear entity boundaries and normalization
Proactively enforces data integrity (e.g., preventing duplicates with constraints)
Takes ownership in ambiguous 0→1 work; doesn’t wait for detailed requirements
Moves work forward with iterative validation
Incorporates feedback early through short loops; avoids late-stage rework
Makes pragmatic speed/quality tradeoffs via phased scope and prioritization
Highly effective backend engineer (9.5/10)
Consistently reliable end-to-end delivery
Strong at clarifying requirements and expectations
Solid validation and structured error handling
Collaborates well with frontend and DevOps
Good engineering judgment on scalability and operational readiness
Improved system reliability and reduced production issues
Led an LLM-driven metadata enhancement pipeline
Strong prompt engineering with structured prompting
Built evaluation-driven systems; minimizes hallucinations