AI/ML Systems Engineer and Full-Stack Developer with a Master's degree in Computer Science (GPA 3.96/4.00) and a rigorous academic background in Physics. Specialized in engineering production-grade RAG architectures, high-concurrency spatial data mining, and high-throughput asynchronous microservices.
High-throughput dual-embedding vector search, non-blocking Redis BullMQ task ingestion, and explainable Machine Learning.
Based in Semarang, Indonesia. Open for technical architecture, AI systems engineering, and research-to-production initiatives.
Engineered an asynchronous background ingest pipeline achieving fast non-blocking HTTP response times (<180ms) and zero-text PDF short-circuiting. Features structured LLM parsing for PII-safe candidate extraction, dual-embedding vector search (384-dim), and deterministic 4-factor scoring with missing mandatory skill penalties.
“Combining empirical discipline from natural sciences with modern software engineering helps foster a first-principles perspective when evaluating emerging AI techniques.”
“Evaluating AI integrations requires balancing rapid time-to-market against clear compliance agreements, zero-retention policies, and long-term data governance.”
“Architecture choices are often best evaluated not by the sheer sophistication of the stack, but by system reliability, maintainability, and predictable operational behavior.”
