Overview

Professional Experience

Production GenAI at IBM, Machine Learning at UAX, and earlier cloud architecture at Huawei.

IBM

AI Engineer

IBM

Customer-facing AI Engineer building production GenAI for IBM enterprise clients — RAG quality, agents, evaluation, and delivery patterns teams can adopt.

  • Shipped production RAG to 100+ enterprise users: Elasticsearch multi-stage retrieval, cross-encoder re-ranking, and offline evaluation of chunking/indexing before go-live; event-driven ingestion and containerised FastAPI services kept knowledge bases current and reliable in production.
  • Gave client teams day-to-day access to proprietary documentation via AI assistants and IDE integrations (MCP servers), reducing friction for engineers querying complex content.
  • Cut developer time by 30% with multi-agent GenAI workflows for code generation, self-correction, and unit testing—shipped from prototype to production on cloud infrastructure.
  • Trained 100+ customer engineers as technical advisor, turning GenAI designs into patterns teams could adopt in production.
  • Led autonomous-agent POC for senior leadership and customer stakeholders, securing buy-in for next-step adoption.

UAX

University Professor in Machine Learning

UAX University

Associate Professor designing and delivering Machine Learning I & II for the undergraduate AI & Computation degree — theory, labs, and projects that map to real applied workflows.

  • Associate Professor of Machine Learning I & II in the AI and Computation degree (20+ students): own and deliver both courses end-to-end—syllabus, lectures, labs, and assessment—covering supervised and unsupervised learning, model evaluation, and applied ML workflows.

Huawei

Junior Solutions Architect

Huawei

Solutions Architect for hybrid-cloud and AI workload deals — translating stakeholder needs into approved architectures with clear cost, reliability, and operational trade-offs.

  • Closed hybrid-cloud engagements by translating stakeholder requirements into approved architectures with clear cost and operational trade-offs.
  • Migrated startup AI workloads to public cloud, clearing infrastructure bottlenecks so the team could run inference and serve assets reliably from a single footprint.
  • Productionised ML inference for a software company under bursty load—autoscaling and queueing so jobs completed reliably during traffic spikes.
  • Delivered backup solution for a telecommunications consulting firm: defined recovery objectives and paired technical design with economic proposals for stakeholder approval.