Machine Learning Engineer at Provectus

RESPONSIBILITIES

● Design, build and maintain MLOps pipelines on AWS and GCP ecosystems.

● Architect natural language processing pipeline for processing news articles to enable intelligent risk mitigation.

● Create RESTful APIs with a Python backend to serve AI-powered applications.

● Design and develop a robust backend system for a specialized business networking platform in the financial sector.

KEY ACCOMPLISHMENTS

● Implemented a high-performance MLOps pipeline to enable supply-chain users to gain valuable insights into their operations through natural language. The system processes over 1M+ records daily, resulting in a 25% improvement in query response time and significantly increasing overall productivity.

● Developed an information extraction pipeline for processing 100k+ news articles per day, enabling users to identify potential risks in their supply chains.

● Created a scalable backend system for a financial networking platform supporting 10k+ concurrent users with an average response time under 50ms, resulting in increased user engagement and business revenue.

● Mentored 5 engineers in best practices for machine learning development, reducing time-to-market for new features by 20% and improving code reliability.