• Design, build, and implement scalable software solutions supporting deployment of machine learning workloads on hardware accelerators used in production environments.

• Develop internal platforms and automation tools using Python and JavaScript to improve developer

workflows, testing reliability, and deployment efficiency.

• Deploy and maintain services across cloud and containerized environments using Docker, Kubernetes, and

CI/CD pipelines, improving release stability and observability.

• Rapidly prototype new engineering approaches for model deployment and runtime optimization, enabling faster

experimentation and iteration for applied ML products.

• Collaborate with cross‑functional teams including infrastructure engineers, data scientists, and product

stakeholders to deliver monetizable platform capabilities.

• Lead the creation of automated testing frameworks and performance monitoring systems, increasing code

coverage and identifying regressions earlier in the development cycle.

• Built and deployed distributed backend systems used by enterprise customers, designing services in Java,

Python, and JavaScript across cloud infrastructure.

• Implemented scalable data processing pipelines on Google Cloud Platform, handling large datasets and

reducing processing latency by 40%.

• Designed and launched production‑grade REST APIs enabling new product features and external

integrations for partner platforms.Worked across multiple frameworks and internal tools to deliver solutions in

real‑world financial and enterprise software environments.

• Collaborated with global cross‑functional teams including product managers, QA engineers, and client

stakeholders to ship customer-facing functionality.Participated in production on‑call rotations, diagnosing

infrastructure and application issues and implementing long‑term reliability improvements.