AI Backend & Cloud Data Engineer | Python, Golang, AWS, PostgreSQL

Backend systems, cloud infrastructure, and data workflows need more than clean code. They need the right architecture, stable data flow, predictable deployment, and enough observability to survive real users, real records, and real business pressure.

My work focuses on Python and Golang backend systems, AWS cloud infrastructure, PostgreSQL database architecture, API integrations, AI workflow integration, and data pipeline reliability. I work across FastAPI, REST APIs, microservices, AWS Lambda, EC2, S3, RDS, ECS, Docker, CI/CD, PostgreSQL, Redis, SQL optimization, OpenAI API, vector search, RAG pipelines, and internal automation tools.

A typical project may involve replacing manual spreadsheet workflows with an API-driven backend, moving a fragile prototype into AWS, redesigning a slow PostgreSQL schema, connecting several third-party systems, or adding LLM features that actually fit into an existing business process. The important part is not only making it work once, but making sure the system is maintainable, secure, traceable, and ready for future changes.

One area where I bring strong value is diagnosing weak points in existing systems. Slow queries, unclear data models, broken API handoffs, missing logs, unreliable background jobs, cloud misconfiguration, and AI features with poor retrieval logic are common problems that can quietly damage a product. I focus on finding those issues early and solving them with practical engineering decisions instead of unnecessary complexity.

Clients usually work with me when they need someone who can understand the backend, database, cloud environment, and business workflow together. That means fewer gaps between development, deployment, data accuracy, and long-term maintenance.