Aug 2022 – May 2023 · 9 mos · Bengaluru, Karnataka, India

• Spearheaded the modernization of a legacy monolithic system by architecting and developing two essential, high-performance Django microservices (Outbound Campaign Manager & Telephony Provider Service), thus allowing the organization to adopt a cutting-edge, microservices-driven architecture for increased scalability, flexibility, and maintainability.
• Revolutionized the architecture of these microservices by integrating asynchronicity by using AWS SQS (and Kinesis) for efficient task queuing, and AWS Lambda functions for task processing, thus elevating overall system responsiveness, resilience, and adaptability.
• Leveraged PostgreSQL as a robust RDBMS in a master-slave configuration, thus optimizing the system to accommodate high read rates demanded by the service's application servers, thereby significantly boosting performance, reliability, and data availability in a demanding environment.
• Developed several private and public REST APIs as components of these microservices, catering to both internal and external clients. Drove changes in other services to treat the new microservices as the ultimate source-of-truth, thus facilitating transition away from the old monolith.

Nov 2018 – Aug 2022 · 3 yrs 9 mos · Gurugram, Haryana, India

• Built stand-alone core microservices in Django to generate aggregated time-series data (called metrics) on the basis of millions of incoming IoT data streams from edge devices. [Outcome: Deprecation of monolith in favour of the new microservice. Reduced coupling. Increased cohesion.]
• Built several private & public REST APIs as part of this microservice for internal and for external clients. Drove changes in other services to treat the new microservice as ultimate source-of-truth about metrics data. [Outcome: Improved consistency due to a single source-of-truth. Reduced coupling. Increased cohesion.]
• Used Apache Kafka to send this app's generated data to multiple databases for persistence: PostgreSQL and ReadingDB (an obscure time- series Database). Imbued fault-tolerance & reliability in Kafka consumers by using techniques such as Exponential Backoff, dynamic adapting of data-transfer-rate on basis of destination's health etc. [Outcome: Improved reliability & fault-tolerance.]
• Used Redis to cache this microservice's queried data. Used LRU as a cache replacement policy. [Outcome: A speedup of 8 times in the response of the API used to query data from this microservice.]
• Dockerized the app and deployed it on Kubernetes. [Outcome: Isolation of environments. Modernised architecture & Infrastructure.]
• Migrated 200 Million metrics data-points from legacy ReadingDB to PostgreSQL in a microservice; zero downtime and no disruption to prod services.[Outcome: Enabled the new microservice to become the single-source-of-truth as now it has the historical metrics data too.]
• Optimized company's cloud infrastructure via application rearchitecture, service elimination, 90% Kubernetes workload transition, and cheaper cloud migration [Outcome: achieved 10x cost reduction.]
• Developed data pipeline with Apache Kafka and Python consumers for IoT device streaming [Outcome: Streamlined & modernised architecture.]

Jun 2016 – Oct 2018 · 2 yrs 4 mos

• Automated the build and deployment process by using the Pipeline as code functionality in Jenkins and ansible features such as playbooks for deployment and configuration management. (Tools used: Docker, Jenkins, GIT, Python and Ansible)
• Managed and improved the AWS Cloud infrastructure including EC2, RDS, S3, ELB, IAM, Autoscaling group, EFS, EBS Volumes etc. on behalf of business clients. (Tools used: Python, AWS Cloud stack)
• Responsible for developing, maintaining, designing the CI/CD systems to get the quick feedback for the S/W development which include the source code sync in GitHub, compilation, test execution and report generation. (Tools used: Python, git, Github, Docker, Ansible, Jenkins)
• Built components of software pieces for business clients in Python, and aided the development process by using VCS tools like git, GitHub etc.
• Regularly brainstormed about the architecture of the application by collaborating with dev team and then designed the infrastructure for that application accordingly. Provided solutions to the existing problems in the infrastructures of our clients and actively thought about the future potential improvements that can be done.