I’m a Senior Technical Support Engineer with more than seven years of experience across enterprise applications, technical documentation, and customer onboarding. Alongside industry work, I’m pursuing doctoral research on abstractive text summarization with large language models for low-resource languages.
Whether you are evaluating my technical experience or exploring my research, the relevant work is one click away.
Enterprise application support, SLA-driven incident and escalation management, technical documentation, customer onboarding, and AI-enabled support workflows.
Doctoral research focused on abstractive text summarization using large language models for low-resource languages, alongside five publications.
My work spans enterprise support, AI-assisted workflows, technical knowledge, and research on summarization for low-resource languages.
Building Python and Microsoft Copilot workflows to reduce manual effort in ticket triage and help support teams process incoming issues more efficiently.
Technical support experience →Applying AI to knowledge-base drafting, with technical accuracy and human review remaining central to the documentation process.
Technical support experience →Supporting business-critical applications through incident, change, and escalation management, with a focus on SLA commitments and clear stakeholder communication.
Technical support experience →Doctoral research into the use of large language models for abstractive text summarization in low-resource language settings.
Research focus →Five research publications complement the ongoing doctoral work. The full bibliographic record is available through Google Scholar.
Browse publications →Work at the intersection of language technology and summarization, with an emphasis on challenges that arise when training data and language resources are limited.
Research publications →My PhD research examines abstractive text summarization using large language models in low-resource language settings.
My doctoral work examines how large language models can produce useful abstractive summaries in low-resource language settings. This area raises practical questions about data scarcity, summary quality, and how well methods transfer across languages.
View publications ↗I have published five research papers. The full and current bibliographic record is available on Google Scholar.
For complete titles, author order, publication venues, and citation details, see the linked Google Scholar record.
I bring together hands-on experience supporting enterprise software and doctoral research in natural language processing. Both areas depend on understanding complex information and making it useful to the people who need it.
7+ years in enterprise application support, technical documentation, and customer onboarding across ERP, PLM, and SaaS products.
Three years supporting business-critical environments, including incident, change, and escalation management against SLAs using Jira and Zendesk.
Researching abstractive text summarization using large language models for low-resource languages, with five publications to date.
For publication details and research updates, visit my Google Scholar profile. For professional discussions, please use the contact details listed on my academic or professional profiles.