Architect and lead enterprise-scale AI, GenAI and Agentic AI solutions across Knowledge Management, Data Engineering, Data Management and Application Development use cases, from solution design and PoC/MVP through implementation to production.
Evaluate models and methodology: Review AI use cases, data suitability, modelling assumptions, validation methods and performance measures. Conduct reproducible tests of accuracy, reliability, robustness, fairness and explainability, with findings documented for each assigned assessment.
4+ years designing corporate training, onboarding, or learning and development programs with direct ownership of facilitator guides and curriculum outlines
Pathfind together with internal experts on automation of engineering data: explore with guidance from supervisor and technical experts enhancements of engineering data using shell scripts, python scripts and commercial tools such as schedulers (Jenkins), version control (Gitlab), requirements (JAMA) and anomaly tools (JIRA).
Lead / support end-to-end data science projects, including cleaning of source data and working with large data sets (processing, manipulation, analysis, and modeling). Translating data into useful insights to drive business decisions in the area of production management.
Lead the Development of Enterprise AI Agents Design, build, and deploy production-ready AI agents and multi-agent systems that automate complex business processes, augment decision-making, and deliver measurable operational impact across Finance and related business functions.
Design, build, and maintain ML infrastructure and tooling to support the full ML lifecycle (data preparation, training, evaluation, deployment, monitoring, and retraining).