Python Developer

Worked on a Python-based backend platform using Flask and FastAPI to process large volumes of Salesforce CRM data (leads, opportunities, accounts, activities), supporting internal pipeline tracking, revenue analytics, and sales reporting across multiple teams. Built and maintained modular microservices for lead scoring, opportunity prioritization, customer segmentation, revenue analytics, and anomaly detection, exposing REST APIs that reduced analytics turnaround time by 30%. Implemented real-time, event-driven pipelines using Apache Kafka to stream CRM change events and activity updates, reducing data latency from hours to minutes and improving pipeline visibility for sales managers by 40%. Integrated with Salesforce REST, Bulk, and Streaming APIs, synchronizing millions of CRM records daily and improving data completeness and consistency across analytics platforms by 25%. • Developed and deployed machine learning models using scikit-learn, TensorFlow, Pandas, and NumPy for lead conversion prediction, deal win probability scoring, churn risk analysis, and revenue leakage detection, improving forecast accuracy by 15–20%. Implemented asynchronous ML inference services using FastAPI and asyncio, handling high volumes of real-time prediction requests with low latency and high reliability during peak business hours. • Managed data storage and retrieval using PostgreSQL for structured CRM and revenue metrics, MongoDB for semistructured activity logs, and Redis for caching, reducing dashboard load times by 35%. • Built internal, non-customer-facing analytics dashboards using React.js, TypeScript, Redux, HTML5, and CSS3, enabling leadership to quickly assess pipeline health, forecast variance, and regional sales performance. Deployed and scaled services on AWS (EC2, S3, Lambda, RDS) using Docker and Kubernetes (EKS), improving system reliability and supporting smooth scaling during quarter-end sales spikes.