Built and optimized Python-based backend services and REST APIs (Django, DRF, Flask) to support financial analytics, risk modeling, and customer data platforms.

Designed scalable data pipelines using Pandas, NumPy, SQL, Spark, and Airflow to ingest, clean, and process large-scale financial datasets.

Partnered with data science teams to productionize machine-learning models (Scikit-learn, PyTorch), improving inference performance for real-time and batch workloads.

Developed NLP workflows using NLTK and spaCy for text analysis and classification in data-driven applications.

Deployed and supported cloud-native applications on AWS and Azure using Docker, Kubernetes, and CI/CD pipelines while following security and compliance best practices.

Built and scaled backend services using Python, Django, and DRF for an omnichannel e-commerce platform, developing REST APIs for product catalog, inventory, orders, and pricing modules.

Optimized data workflows and performance by writing efficient PostgreSQL/MySQL queries, leveraging Pandas for data validation, and implementing Celery + Redis background tasks, reducing API latency and improving transaction efficiency.

Integrated payment gateways and logistics APIs, applied JWT-based authentication & RBAC, containerized applications with Docker, and ensured production stability through testing, monitoring, and Agile collaboration.