Bahzad Khan

Delivered an internal AI assistant using LangChain with RAG and a vector database to reduce analyst manual data lookup time and improve team productivity. Modernized ETL into event-driven pipelines with Kafka/Airflow/dbt to increase data freshness from daily to near real-time. Built a real-time analytics dashboard using Streamlit/FastAPI/ClickHouse to provide stakeholders sub-second visibility into operational metrics. Improved database performance by ~30%, enhancing application responsiveness for end users. Reduced engineering cycle time by driving AI-assisted development adoption across the team using tools like GitHub Copilot and Claude. Delivered secure, well-governed internal and client-facing REST/GraphQL APIs with OAuth2/OIDC and OWASP-aligned practices. Implemented Kubernetes autoscaling to cut infrastructure costs and mentored engineers while aligning platform investment to business priorities.

Automated reporting workflows using pandas/NumPy to free analyst time for higher-value work. Delivered REST APIs integrated with React/JS frontends to speed product feature delivery. Improved application performance via PostgreSQL/MySQL query and schema optimization. Built internal tools in Flask/Django to support business process automation. Reduced integration friction for frontend teams through clear API contracts and OpenAPI/Swagger documentation. Improved deployment reliability by automating infrastructure with Terraform/Docker and adopting Kubernetes. Reduced defect escape rate with expanded pytest coverage and rigorous code review, and maintained production stability via proactive monitoring and issue resolution.