Expertise
I didn't arrive at AI platforms from a framework tutorial — I built up to them: logs, ETL, data lakes and BI first, then model serving, retrieval, evaluation and agents on top. That arc is why the platform holds together.
Market intelligence, forecasting and statistical analysis for international clients; leading analyst teams and learning what decision-makers actually need from data.
The pivot to Europe and to engineering-grade data work.
ETL and log pipelines (NiFi, MiNiFi, Talend) feeding HDFS/Hive data lakes and search analytics (Elasticsearch, OpenSearch, Solr, Kibana); dashboards and KPIs on top.
Pioneered the organisation's first internal chatbot, benchmarking open-source LLMs and defining the GPU/VRAM requirements that justified real AI infrastructure.
Enterprise AI cluster across multi-server multi-location GPU infrastructure: vLLM serving behind a LiteLLM proxy, with Graph RAG, specialised OCR/translation/transcription/vision services, MCP-based agentic workflows, automated fine-tuning cycles — with MLflow evaluation and Prometheus/Grafana monitoring throughout.
Drag, zoom, and hover to trace how the pieces connect. The gold dashed path is the career evolution above.