9+ years of software engineering experience designing and building backend services, high-throughput REST APIs, and scalable distributed architectures using Python and AWS — actively exploring modern GenAI and agentic systems.
I am a Senior Backend Engineer with over 9 years of software engineering experience, specializing in architecting, developing, and operating resilient backend systems, REST APIs, and microservices at scale.
My core foundation centers around Python (FastAPI, Django, Django REST Framework, Flask), distributed system design, and database-driven applications. Throughout my career, I have taken end-to-end technical ownership of backend platforms—from schema modeling across PostgreSQL, MongoDB, and Redis to optimizing asynchronous workloads, caching strategies, and high-availability cloud deployments on AWS.
My engineering journey originated in geospatial computing and spatial data engineering, where I engineered spatial queries, data processing pipelines, and PostGIS solutions. That early data-intensive foundation catalyzed my natural progression into large-scale backend engineering, API architecture, and distributed services.
I emphasize production-grade engineering: clean application architecture, measurable system performance, clear domain boundaries, and pragmatic automation that keeps mission-critical services highly available and maintainable.
A deep focus on backend engineering, system scalability, and cloud architecture.
Real-world systems highlighting backend architecture, API engineering, databases, and scalability.
Training and evaluation laboratory platforms needed a robust, high-throughput backend to orchestrate concurrent training sessions, laboratory equipment telemetry, and state synchronization without latency bottlenecks.
Architected modular backend services utilizing Django and FastAPI to handle laboratory session workflows, telemetry ingestion, and simulation state coordination across multi-tenant environments.
Designed asynchronous REST endpoints, integrated Redis caching and state storage, structured MongoDB and PostgreSQL schemas for simulation data, and containerized microservices orchestrated with Docker Swarm and MinIO S3 storage.
Enterprise production environments suffered from fragmented alerting streams and manual triage overhead during operational anomalies, necessitating high-speed, reliable incident ingestion and self-healing execution.
Engineered a centralized, event-driven backend service in FastAPI that ingests alerts, evaluates remediation runbooks programmatically, and initiates automated remediation actions with full audit observability.
Implemented asynchronous webhook ingestion endpoints, Redis-backed event deduplication queues, PostgreSQL for state and runbook definitions, and integrated Prometheus/ELK for comprehensive diagnostic tracing.
Transportation fleets and mission-critical enterprise systems required resilient automated backup coordination, strict data verification, and real-time tracking pipelines operating with high availability.
Developed serverless and containerized backend components coordinating automated snapshotting, scheduled backups, and live asset tracking endpoints with high data integrity guarantees.
Leveraged AWS Lambda, S3, and Boto3 for event-driven storage operations, built robust REST APIs backed by MongoDB and Redis for low-latency operational data, and deployed scalable container workloads via Docker.
A high-volume B2B trade marketplace required fast catalog querying, secure business communication APIs, and resilient data processing pipelines for high concurrent merchant traffic.
Developed backend APIs and business logic services using Python and Django to power supplier-buyer matching, inquiry processing, and transaction management.
Designed optimized relational schemas in PostgreSQL, implemented caching layers with Redis, built scalable REST endpoints, and automated background data processing workflows via shell scripting and Linux daemons.
High-scale map datasets and location services required heavy spatial transformations, geofencing computations, and spatial index querying over massive geographic coordinates.
Engineered automated Python data processing workflows, spatial queries, and backend scripts to ingest, transform, and structure spatial layers for geographic search and visualization.
Authored high-performance spatial SQL procedures in PostGIS/PostgreSQL, automated large-scale dataset conversions using Python, and integrated ESRI ArcGIS and QGIS spatial databases.
9+ years of progressive software and backend engineering leadership across enterprise environments.
Drive backend architecture, microservices development, and scalable cloud engineering using Python and AWS:
Led backend and web application development projects using Python, Django, and modern API frameworks:
Focused on REST API design, backend database integration, and cloud-backed deployments:
Engineered backend services and business features for India's leading B2B e-commerce platform:
Formed foundational data engineering and spatial analysis expertise that transitioned into full software engineering:
Beginning of professional technical career: spatial data analysis, automation scripts in Python, and database manipulation using SQL.
Applying 9+ years of backend architecture and system-design depth to modern LLM and agentic systems.
Currently exploring and building with modern LLM and agentic AI technologies, applying my backend engineering and system-design experience to AI applications.
LLM application development, prompt composition, tool integrations, and retrieval pipelines.
Stateful and agentic workflow orchestration, cyclical execution graphs, and multi-agent coordination.
Tracing, debugging, latency analysis, and systematic evaluation of production LLM applications.
Interested in discussing scalable backend architecture, Python engineering, or new opportunities? Let's connect.