AI-focused Information Technology Engineering graduate (2024) building independent, end-to-end AI systems across agentic RAG, time-series forecasting, and computer vision. Hands-on with data pipelines, model integration,…
AI-focused Information Technology Engineering graduate (2024) building independent, end-to-end AI systems across agentic RAG, time-series forecasting, and computer vision. Hands-on with data pipelines, model integration, FastAPI services, evaluation, automated testing, and reproducible Docker-based delivery.
<p>Owned the end-to-end delivery of self-directed AI projects, from problem definition and data preparation to model/pipeline design, API integration, testing, performance analysis, and documentation.<br>
Engineered LLM applications using LangGraph, LangChain, and Qdrant; implemented hybrid retrieval, reranking, stateful tool workflows, structured output validation, permission checks, and PII masking.<br>
Developed ML and computer vision workflows for time-series spending forecasts, multi-timeframe market data, and retail video analytics; addressed data quality, temporal leakage, tracking, and inference throughput.<br>
Built FastAPI REST services and user-facing web/Streamlit interfaces; containerized applications with Docker Compose and used pytest, Ruff, and GitHub Actions to support repeatable development and testing.<br>
Evaluated AI behavior using scenario-based checks, automated test suites, groundedness and latency measurements, and video throughput benchmarks; documented limitations and failure cases.</p>
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