No cost, no commitment - we'll make a personal intro
RK
Rangasai Kumbhashi Raghavendra
Software Engineer specializing in cloud, microservices, and enterprise SaaS
Motorola SolutionsStevens Institute of TechnologyJersey City, NJ6 Years ExperienceIntern LevelWorks On-Site
Connect with Rangasai
Rangasai already has a relationship with Reval, so a warm intro from us gets a much better response than cold outreach.
Typically responds within 24 hours
Recommended
Already have an account?
About
JavaScript/Node.js engineer with open-source contribution experience (Mongoose) focused on connection pooling, test reliability, and memory/resource management. Has diagnosed and fixed real-world performance issues in an insurance claims application and improved resilience via failover DB design. Also experienced producing compliance/governance documentation for an EU-based biopharma, enabling stakeholders to make decisions quickly amid changing regulations.
Hire with Reval
Find your next great hire
Our AI agents source, screen, and vet candidates for your open roles. Get qualified candidates within 48 hours.
Junior Software Engineer specializing in full-stack and ML/NLP systems
New York City, NY2y exp
IBMUniversity of Massachusetts Amherst
“Entry-level full-stack engineer with internship experience at Amazon (Appstore IAP flow + uninstall recommendation workflow) and a health-tech startup (OneVector) where they built a DSUR reporting workflow end-to-end, including document generation, S3-backed versioning/metadata, and secure preview/download. Demonstrates strong production debugging and reliability mindset (instrumentation, deterministic retrieval, idempotent writes) and focuses on UX/performance in high-stakes user flows.”
Junior Software Development Engineer specializing in backend data platforms and LLM applications
New York, NY3y exp
AmazonNortheastern University
“Amazon internship experience building and shipping an end-to-end NL-to-SQL system: ingested/normalized metadata across 60+ internal tables, added rigorous multi-layer validation for LLM-generated SQL, and served it via a FastAPI backend for engineers—driving 90%+ faster dataset discovery and ~70% lower effort to access data. Also built an early-stage RAG-based healthcare assistant, iterating on chunking, embeddings, and retrieval to improve answer quality post-launch.”