Anees S

About Anees S

Software engineer with 3+ years of hands-on experience supporting production-grade cloud systems, data pipelines, backend services, and AI/ML-supporting workflows in enterprise environments.I currently work at Meta (via Qualitest), where I support cloud-based systems and data workflows for AR/VR and AI-driven products. My work focuses on building and automating Python-based data validation and processing workflows, integrating with cloud platforms, internal frameworks, and databases, and improving reliability and efficiency across large-scale testing and analytics environments.Previously, I’ve worked across cloud engineering, data engineering, and designing and supporting systems on AWS, automating infrastructure with Terraform, deploying containerized workloads, and enabling ML-driven use cases through reliable data pipelines and structured workflows.

Experience

1. Graduate Engineer Via Qualitest

   Meta

   Sep 2025 — Present · Seattle, WA, US

   Designed and automated Python-based data validation and processing workflows, improving data integrity and accelerating device testing cycles across large-scale AR/VR R&D environments.•Developed modular Python utilities and reusable data-processing components to standardize test outputs, reduce manual intervention, and improve overall system efficiency.•Integrated automation with cloud-based systems, internal test frameworks, and relational databases, enabling reliable data ingestion and near–real-time reporting across multi-device environments.•Supported cloud workflows and data pipelines used for testing, analytics, and AI/ML-supporting processes, ensuring consistency and reliability across environments.•Collaborated with software engineers, QA, and platform teams to troubleshoot data, application, and environment-related issues, reducing testing bottlenecks and rework.•Applied strong software engineering and analytical skills to improve pipeline performance, data quality, and operational reliability in a fast-paced enterprise setting.•Contributed to AI/ML evaluation workflows by preparing, validating, and organizing structured datasets used in model testing and performance analysis.

Education

• Northern Arizona University

  Master of Science - MS, Information Technology