Python, FastAPI, AWS, Postgres, Redis, RabbitMQ, Apache Kafka, Docker, Domain Driven Design, Microservices, DynamoDB, AWS RDS — SQL, data modelling, Distributed Systems, High-Performance Async Processing, React, System D…
Python, FastAPI, AWS, Postgres, Redis, RabbitMQ, Apache Kafka, Docker, Domain Driven Design, Microservices, DynamoDB, AWS RDS — SQL, data modelling, Distributed Systems, High-Performance Async Processing, React, System Design, Celery, Agile, Scrum, asyncio and multiprocessing, (Blue-Green, Canary)
Built a multi-tenant workflow orchestration platform supporting secure, auditable execution of long-running asynchronous workflows, and scalable backend task processing.
Designed an asynchronous execution pipeline using Celery and RabbitMQ, enabling low latency responses in less than 100ms while processing executions reliably with background workers.
Implemented Redis-based execution state management for workflow status, progress, and timestamps, enabling frequent client polling with single-digit millisecond latency.
Optimized system scalability by reducing database read load by ~70% through Redis caching and TTL-based transient state management.
Designed resilient state synchronization across Redis and PostgreSQL to support fault recovery, observability, and reliable distributed execution.
Built workflow execution patterns compatible with scalable AI/ML pipeline orchestration and long-running processing workloads.
Designed and developed scalable Python-based microservices and REST APIs for high-throughput business workflows and analytics-driven backend processing.
Built microservices-based architecture deployed on AWS (ECS, EKS, Lambda) with clear separation of concerns and independent scalability.
Designed event-driven asynchronous processing workflows using SQS and distributed workers, improving throughput by 3× and reducing API latency.
Owned database design and optimization using PostgreSQL, DynamoDB, and Redis, including indexing strategies, partitioning, caching, and query tuning.
Implemented authentication and authorization using OAuth2, JWT, AWS Cognito, and role-based access control (RBAC).
Built backend services optimized for integration with asynchronous processing and data-driven workloads in distributed cloud environments.
Built a multi-tenant workflow orchestration platform supporting secure, auditable execution of long-running asynchronous workflows, and scalable backend task processing., Designed an asynchronous execution pipeline using Celery and RabbitMQ, enabling low latency responses in less than 100ms while processing executions reliably with background workers., Implemented Redis-based execution state management for workflow status, progress, and timestamps, enabling frequent client polling with single-digit millisecond latency., Optimized system scalability by reducing database read load by ~70% through Redis caching and TTL-based transient state management., Designed resilient state synchronization across Redis and PostgreSQL to support fault recovery, observability, and reliable distributed execution., Built workflow execution patterns compatible with scalable AI/ML pipeline orchestration and long-running processing workloads.
Designed an event-driven notification system for emails, push notifications, and internal alerts.
Built producer-consumer workflows using SNS + SQS fan-out pattern.
Implemented retry, DLQ handling, and failure recovery for message processing.
Exposed APIs to configure notification preferences and templates.
Developed a centralized access management platform enabling secure JWT-based authentication and role-based authorization across multiple backend services.
Engineered a scalable, high-throughput audit logging system to record user activities and API interactions, supporting regulatory compliance and security monitoring.
Implemented secure integrations with AWS Cognito and IAM to enforce least-privilege principles and ensure robust service-to-service authentication.
Designed an event-driven notification system for emails, push notifications, and internal alerts., Built producer-consumer workflows using SNS + SQS fan-out pattern., Implemented retry, DLQ handling, and failure recovery for message processing., Exposed APIs to configure notification preferences and templates.
Project: Workflow Orchestration PlatformMarch 2024 to PresentPython | FastAPI | Celery | RabbitMQ | Redis | PostgreSQL | SQLAlchemy | Docker | CI/CD | pytest1) Led development of a mission-critical, multi-tenant workflow orchestration platform — engineered for high availabilityand low latency using Python asyncio and multiprocessing, with full ownership of design, security, and productiondeployment.2) Designed an asynchronous execution pipeline using Celery and RabbitMQ enabling sub-100ms latency — applied deepPython concurrency expertise (asyncio, background workers) to build memory-efficient, high-performance processing atscale.3) Implemented Redis-based execution state management for workflow tracking — defined SLI/SLO metrics and builtobservability into the platform using structured logging and monitoring, enabling rapid incident identification and rootcause analysis.4) Identified and resolved hidden performance bottlenecks through profiling and data analysis — reduced database readload by ~70% via Redis caching; performed risk assessments and evaluated remediation alternatives for system reliabilityand security.5) Designed resilient state synchronization across Redis and PostgreSQL for fault recovery — implemented secretmanagement and access controls conforming to enterprise security standards, ensuring confidentiality, integrity, andavailability.6) Conducting high-level code reviews — standardized Python and DevOps best practices across the team; collaboratedwith ML teams to integrate AI pipeline orchestration into production-grade workflow execution patterns.
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