Engineering Practice for Deployable AI Systems

Backend Systems · Deployment Workflows · GPU Engineering Exposure

Engineering experience across backend services, deployment environments, and GPU-oriented AI systems, supporting my ability to connect research concepts with deployable software.

Proof: Backend Internship · Docker · GraphQL · VPS Deployments · GPU Engineering Exposure

Context

AI research engineering requires more than model-level thinking. It also depends on APIs, backend systems, deployment environments, infrastructure, and runtime constraints.

Experience Areas

Backend Systems — backend services for mobile and admin applications using Java, GraphQL, Spring Boot, Redis, Docker, and Domain Driven Design.

Deployment Workflows — VPS deployment, containerized environments, databases, and service configuration across projects.

GPU Engineering Exposure — exposure to LLM inference and GPU-oriented engineering in a professional context.

My Role

My role varied across internship and project contexts, from backend implementation to deployment support and technical exploration.

Contribution

This experience strengthened my ability to understand applied AI systems beyond the research layer: APIs, data flow, deployment, service design, and infrastructure decisions.

Outcome

This section supports my positioning as an AI Research Engineer who can move between research concepts, prototypes, and deployable systems.

Media

Architecture diagram · Sanitized API flow · Docker/deployment diagram · GraphQL schema excerpt · Anonymized app screenshots · Technology strip