Shielded crucial financial services by deploying JWT authentication to reinforce API security, achieving a 99.99% API

uptime and satisfying SOC2 compliance within the second quarter.

Initiated Redis caching mechanisms for critical analytics endpoints, accelerating response times by 30% during peak load

periods and delivering a more responsive user experience.

Pioneered the transition to a Flask based backend microservice using AWS Lambda and API Gateway, minimizing

infrastructure management overhead by 30% and speeding up development cycles for the team.

Orchestrated project workflows and sprint ceremonies using Jira, enhancing team collaboration and on-time delivery of key

features aligned with business objectives, boosted feature release velocity by 20%.

● Revolutionized data retrieval, integrating a robust web crawler using Python, Beautiful Soup, and requests enhancing

data feed efficiency to the LLM pipeline by 60% and improving speed by 45%.

● Launched a Vector Embeddings Generation service with Django, Apache POI, and PySpark, slashing manual processing

time by 50% and improving data accessibility across the platform.

● Standardized end-to-end pipeline deployments with Python-based scripts, slashing release overhead by 30% and ensuring

consistent, reproducible environments for all project teams.

● Integrated RAG techniques using Python, Elasticsearch, and OpenAI APIs, reducing query processing time by 35% and

improving the scalability of data processing pipelines.

● Engineered NoSQL data architectures using MongoDB for schema-less data repositories, which accelerated query

processing speeds by 30% and supported real-time analytics dashboards for stakeholders.

● Streamlined payment events through Kafka with Python based microservices, enhancing system resilience and scalability

for high-volume processing during peak hours in Q4, a critical time for the company.

• Developed and deployed over 100 RESTful APIs using Spring Boot's ORM, JPA, and Hibernate on AWS.

• Optimized backend infrastructure by implementing caching mechanisms and load balancing, resulting in a notable 40% reduction in response time and improved system performance.

• ⁠Optimized backend infrastructure by implementing AWS Elastic Load Balancer (ELB), AWS CloudFront, and AWS RDS connection pooling, resulting in a 40% reduction in response time and improved system performance.

• ⁠Implemented performance optimizations using AWS CloudWatch metrics and AWS X-Ray tracing, leading to a 20% increase in system throughput.

• Revolutionized Kafka message replay mechanisms to simulate and troubleshoot production issues, improving debugging efficiency by 40%.