Developed backend services in Python (Flask & FastAPI) to support the Skill IQ and Role IQ engines,
processing learner responses, calculating scores, and powering personalized learning recommendations.
Built RESTful APIs in Python for internal tools and user-facing features, enabling seamless data exchange
between learning assessments and the React-based frontend.
Contributed to Pluralsight’s Iris AI recommendation system by designing Python microservices that analyzed
engagement data and dynamically suggested relevant course paths.
Improved state management and API efficiency by refactoring backend endpoints and integrating middleware in
Python-based services, resulting in a 25% drop in frontend error rates.
Established CI/CD pipelines using AWS CodePipeline, Docker, and GitHub Actions to streamline builds, testing,
and deployment of Python applications.
Refactored legacy SQL logic embedded in Python apps for performance – optimizing queries through indexing,
parameterization, and execution plan tuning to reduce latency.
Implemented OAuth 2.0 and JWT authentication in Python APIs to securely manage user sessions and
permissions across multiple internal services.
Supported real-time analytics by exposing Python data services consumed by dashboards, enabling tracking of
learner engagement, skill progression, and completion rates.
Migrated critical services to a microservices-based architecture using Python and Docker, improving service
modularity, deployment speed, and fault isolation.
Built test harnesses and integration test suites using Pytest, helping reduce regression bugs and achieving >90%
coverage on core services.
Collaborated with frontend and DevOps teams to define API contracts, debug integration issues, and ensure
system reliability across releases.
Mentored junior engineers on Python best practices, testing strategies, and RESTful design patterns to
improve team velocity and code quality.