Designed and scaled Python (Django/FastAPI) backend services handling high-volume healthcare transactions, reducing P99 latency by 45% via async I/O, connection pooling, and query plan optimization.

Architected stateless, horizontally scalable microservices with idempotent REST APIs, sustaining 99.9% uptime across distributed deployments.

Increased transactional throughput by 40% by redesigning composite indexing strategies, eliminating lock contention, and tuning execution plans under concurrent workloads.

Implemented fault-tolerant event-driven pipelines (Kafka + Airflow) processing streaming data into Snowflake with retry semantics and failure recovery.

Led on-call production investigations, diagnosing memory bottlenecks and race conditions using profiling, structured logging, and performance tracing.

Led end-to-end data migration to AWS using AWS DMS and Amazon Aurora (MySQL/MariaDB), scaling data volume from 300K

to 500K+ records while ensuring data lifecycle management, governance compliance, and schema consistency.

Designed and implemented automated ETL pipelines to streamline data transfer and integration between GCP and AWS environments,

ensuring high performance, data quality, and scalability within Amazon RDS architecture.

Developed and secured data analytics dashboards for a pension management platform using Google API Management, aligning with

strict data privacy regulations, security protocols, and system compliance standards.

Built and deployed full-stack web applications and RESTful APIs using React (JavaScript/TypeScript), Flask, and FastAPI,

significantly enhancing system responsiveness, usability, and cross-platform functionality.

Collaborated within Agile Scrum teams using Jira to manage sprints and deliverables, achieving 90%+ backend test coverage with

JUnit, Jasmine, and Mocha to ensure system reliability, maintainability, and rapid iteration.

Collaborated directly with the CTO in a fast-paced startup environment to design a B2B SaaS marketplace and optimize the

subscription platform using TypeScript, and Node.js, managing 500+ product listings on AWS S3 and contributing to $1M ARR growth.

Partnered with product managers, data analysts, and QA teams to translate business requirements into scalable technical solutions.

Launched an AI agent answering platform queries via LLMs, RAG pipelines, and TensorFlow, integrated with a GraphQL/Python backend

for subscription tracking, boosting user satisfaction by 35%.

Improved performance of cloud-based services by implementing Redis caching, profiling SQL queries via Prisma ORM/MySQL, and

monitoring system metrics with AWS CloudWatch and Grafana, reducing infrastructure costs by 15%.

Boosted customer engagement 30% by developing responsive full-stack features using React.js, Angular, Node.js, and Express.js.

Reduced SQL execution time 40% by designing normalized schemas, implementing indexes, and tuning database queries.

Cut release errors 25% by automating build and deployment pipelines with GitLab CI/CD.

Lowered production defects 20% by implementing Selenium-driven regression testing suites.

Increased sprint velocity predictability 15% by improving backlog refinement and story-pointing practices.

Enhanced frontend load speed 22% by reducing bundle size, enabling browser caching, and optimizing assets.

Boosted customer engagement 30% by developing responsive full-stack features using React.js, Angular, Node.js, and Express.js.

Reduced SQL execution time 40% by designing normalized schemas, implementing indexes, and tuning database queries.

Cut release errors 25% by automating build and deployment pipelines with GitLab CI/CD.

Lowered production defects 20% by implementing Selenium-driven regression testing suites.

Increased sprint velocity predictability 15% by improving backlog refinement and story-pointing practices.

• Enhanced frontend load speed 22% by reducing bundle size, enabling browser caching, and optimizing assets.