Open terminal profile
Software Engineer with 2+ years of full-time experience building Python backend systems, event-driven AWS services, and cloud infrastructure. I focus on reliable APIs, real-time data pipelines, infrastructure automation, and production observability.
Based in Siliguri, West Bengal, India.
Software Engineer | Jaipur, India
Full-time: June 2024 - Present | Internship: July 2023 - May 2024
- Lead backend development for a real-time train-tracking platform processing cross-account movement events with Amazon Kinesis Data Streams and AWS Lambda.
- Deliver live updates through API Gateway and WebSockets, store live state in DynamoDB, and retain historical movement data in Amazon RDS for PostgreSQL.
- Moved event transformation, aggregation, and UI-ready data preparation into backend APIs to reduce browser workload.
- Implemented event validation, isolated Lambda workflows, and retry-aware processing across asynchronous services.
- Engineered data lake-to-PostgreSQL pipelines and optimized reporting queries.
- Built 3+ scheduled workflows for cleanup, maintenance, and daily snapshots, plus OpenSearch-powered keyword and fuzzy map search.
- Built production observability for serverless and containerized workloads with CloudWatch, ELK, Prometheus, Grafana, and OpenTelemetry.
| Area | Result |
|---|---|
| AWS event processing | Reduced costs by 30% by replacing SQS-based event transport with Amazon Kinesis Data Streams |
| PostgreSQL performance | Cut historical movement query latency by over 97%, from 4-5 minutes to 6-7 seconds, using indexing and table partitioning |
| Deployment automation | Reduced AWS deployment time by 40% using Docker, Jenkins, and Bitbucket CI/CD |
| Infrastructure as code | Reduced Azure infrastructure provisioning time by 60% using Terraform |
| Container platform | Migrated 8+ Docker workloads to Kubernetes and Helm with standardized deployments, versioning, rollbacks, and environment management |
| Reporting pipelines | Reduced reporting-query execution time by 32% |
| AI workflows | Reduced inference costs by 35% with a local CPU-based runtime and added prompt-injection controls |
- Backend & APIs: Python, Django, FastAPI, Flask, REST APIs, Microservices, WebSockets
- Cloud: AWS Lambda, Kinesis Data Streams, API Gateway, DynamoDB, RDS, SNS, SQS, VPC, CloudWatch, Azure
- DevOps & Infrastructure: Docker, Kubernetes, Helm, Terraform, Jenkins, Git, GitLab CI/CD, Bitbucket, Linux
- Databases & Search: PostgreSQL, DynamoDB, Redis, OpenSearch
- Observability: Prometheus, Grafana, ELK, OpenTelemetry, CloudWatch
- AI Workflows: LangChain, LLM Integration, Local Inference
B.Tech. in Computer Science and Engineering
Arya Institute of Engineering & Technology, Jaipur, Rajasthan | 2024
Building reliable backend systems, cloud platforms, and developer-friendly infrastructure.