Professional Experience

Healthcare payer platforms and production SaaS — measurable reliability and performance wins

Software Engineer

Cigna

Aug 2024 – Present
United States
  • Own backend microservices and AWS infrastructure for member-facing healthcare APIs at scale — FHIR R4 REST APIs translating legacy claims and eligibility into HL7 resources for the mobile app, including serialization, bundle pagination, and versioned profiles
  • Built a Golang-based PII/PHI masking layer in API Gateway that redacts sensitive fields (SSNs, diagnosis codes, insurance IDs) before responses reach clients, aligned with HIPAA minimum-necessary practice
  • Design and operate serverless claims processing on AWS — Lambda sizing, DynamoDB access patterns, SQS dead-letter queues, API Gateway throttling — with Terraform and CloudFormation and least-privilege IAM across environments
  • Implement and maintain Pega BPM decision tables for adjudication and claim routing so business teams can update rules independently
  • Lead production incident triage with Splunk across Lambda, RDS, and API Gateway; diagnosed connection pool exhaustion and DynamoDB hot partitions, cutting member login 5xx rate from 3.2% to under 0.5% over two sprint cycles
  • Collaborate across product, compliance, and engineering in SAFe Agile delivery, including architecture reviews with a distributed team across time zones

Software Engineering Intern

Zoho

Aug 2021 – May 2022
India
  • Shipped backend features for a live J2EE SaaS product using layered MVC, from database through API, within Agile sprints
  • Designed a YAML-to-table JDBC data access layer replacing per-entity DAO boilerplate across 14 entities, standardizing CRUD and shortening feature delivery
  • Built authentication and session management: BCrypt (work factor 12), OTP-based 2FA via JavaMail with configurable SMTP TLS, and idle timeout expiry for a multi-tenant production product
  • Improved MySQL performance by removing N+1 patterns in high-traffic modules in favor of indexed JOINs — page loads dropped from about 4.8s to under 200ms on realistic data

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