Great on Implement LRU Cache Algorithm, Difficulty Medium
Great on Understanding Rate Limiting in APIs, Difficulty Easy
Great on Ensuring Real-time Data Accuracy in Mobile App, Difficulty Medium
Great on Reverse Digits of a 32-bit Integer, Difficulty Easy
Great on Word Search in Grid, Difficulty Medium
Perfect on Validate Parentheses in a String, Difficulty Hard
Architected and developed scalable backend systems using Python (Django/FastAPI), handling high-throughput APIs and asynchronous workloads.
Designed and optimized data pipelines for ingestion, transformation, and validation of large-scale enterprise datasets.
Built and maintained RESTful APIs with authentication, rate limiting, and secure integrations with third-party services.
Implemented background task processing using Celery and Redis to improve system performance and reliability.
Optimized database queries and schema design in PostgreSQL to reduce latency and improve overall system efficiency.
Containerized applications using Docker and deployed on AWS with CI/CD pipelines to ensure scalable and fault-tolerant deployments.
Designed and implemented scalable data pipelines to ingest, validate, and enrich enterprise data for AI and LLM-based platforms.
Built Retrieval-Augmented Generation (RAG) frameworks using embeddings and vector databases (FAISS) to improve factual accuracy of AI agents.
Developed secure REST APIs for integrating AI services with internal tools, third-party platforms, and cloud services.
Containerized AI and data services using Docker and deployed on cloud infrastructure (AWS) to ensure portability and scalability.
Implemented data quality checks, logging, and observability to ensure reliable AI outputs in production environments.
Collaborated closely with IT, data, and product teams to align AI solutions with operational and business objectives.
Developed backend services and data APIs using Django and REST Framework for enterprise and AI-enabled applications.
Implemented ETL-style workflows for synchronizing data between PostgreSQL databases and external systems.
Worked with cloud environments (AWS) for deploying and managing backend and data-driven services.
Used Docker for consistent development, testing, and deployment across environments.
Ensured API security, data validation, and error handling following secure-by-design principles.
Designed backend APIs to integrate organizational, scheduling, and transactional data with AI assistants. Implemented RAG pipelines by indexing structured enterprise data into vector databases for semantic retrieval. Integrated WhatsApp-based AI agents with business systems using secure, scalable API pipelines. Deployed services using Docker and cloud platforms to support multi-tenant usage.
Built automated data pipelines for AI-generated product broadcasts distributed via email and WhatsApp. Ensured consistency and validation between transactional databases and AI-generated content.
Implemented preprocessing pipelines for large-scale video datasets used in GAN-based models. Optimized data flow between storage, processing services, and AI inference components.