To apply for this job, you need to complete both steps below:

STEP 1:

Please click the link to submit your application directly to the company: 

Careers

Your application will only be received by Recruiter if submitted via above link. 

STEP 2:

Kindly scroll to the bottom of this page and complete the short VinUni Tracking Form.

Filling out this form alone does not count as applying. Kindly remind this form is not part of the company’s application process. It only helps Careers, Alumni, Industry and Development (CAID) Department discover more opportunities and follow up in case of system issues.

About the Role

The AI Enablement team at Zalopay applies Large Language Models (LLMs) and Natural Language Processing (NLP) to automate and optimise internal operation workflows - reducing manual effort, accelerating processing times, and improving output quality across the business.

As a Product Intern, you will receive well-scoped automation problems from your line manager, translate them into clear product requirements, and rigorously evaluate whether the LLM-powered solutions actually deliver against those requirements. The role is focused on output quality and workflow automation - not UX or user-facing product design.

You will collaborate closely with AI Engineers, Customer Success, and domain teams, sitting at the translation layer between operational requirements and technical implementation. We run disciplined sprint cycles using Jira and Confluence, with daily stand-ups, backlog grooming, sprint planning, and outcome tracking.

What You Will Do

Write Product Requirements

• Receive problem statements and automation opportunities from your line manager, then translate them into structured product documentation - PRDs, user stories, and acceptance criteria
• Break down complex operational workflows into clear, testable requirements that AI Engineers and domain teams can build and validate against
• Maintain requirements documentation in Confluence and track progress in Jira across sprint cycles

Evaluate LLM Outputs

• Design evaluation frameworks to assess whether LLM-powered automation meets the defined acceptance criteria - covering accuracy, consistency, edge case handling, and failure modes
• Build test sets, define scoring metrics, run structured evaluations, and document findings clearly
• Identify gaps between expected and actual model behaviour, and work with AI Engineers to iterate - adjusting prompts, refining criteria, or flagging data quality issues
• Track evaluation results across iterations to measure progress and validate that improvements hold across varied inputs

Collaborate Across Teams

• Work closely with AI Engineers on prompt design, model constraints, and output quality - translating operational requirements into technically actionable specs
• Coordinate with Customer Success, Operations, and other domain teams to gather ground truth labels, validate evaluation criteria, and ensure automated outputs meet operational standards
• Participate in sprint ceremonies - stand-ups, grooming, planning, and reviews - and keep documentation up to date

Requirements

Must Have

• Currently pursuing a degree in Computer Science, Data Science, or Applied Mathematics, with solid CS fundamentals: algorithms, data structures, and working proficiency in Python
• Theoretical understanding of NLP and LLM concepts through coursework - language model architectures, embeddings, transformers, and how LLMs generate outputs. Hands-on experience is a plus but not required
• Strong structured thinking: able to decompose a workflow problem, write unambiguous requirements, and reason carefully through edge cases and failure modes
• Detail-oriented - small specification errors have downstream consequences in automated processes; you take precision seriously
• High initiative and comfort with ambiguity - you will often work in areas without a clear answer and will need to figure things out systematically

Nice to Have

• Hands-on experience with NLP libraries or LLM tooling - Hugging Face, LangChain, LlamaIndex, OpenAI API, or similar
• Familiarity with prompt engineering techniques or LLM evaluation methodologies
• Experience with Jira, Confluence, or similar project tracking and documentation tools
• Prior exposure to operations, process automation, or workflow analysis through coursework or projects

What Makes This Role Valuable

Understand a Fintech Company End-to-End

Customer Support at Zalopay touches every product the company ships - bank linking, payments, transfers, top-ups, and more. Working in this domain means you build a mental model of how the entire business operates, not just one feature. The AI techniques you apply here - NLP, and eventually image and voice processing - are the foundation for a wide range of problems you will encounter anywhere in the industry.

Master LLM Evaluation

Knowing how to rigorously evaluate LLM outputs - defining metrics, building test sets, identifying failure modes - is one of the most in-demand and underdeveloped skills in applied AI. You will build this capability hands-on.

Fast Growth Environment

Zalopay moves fast. You will be mentored by experienced PMs and AI leads, given real ownership of deliverables from day one, and evaluated on outcomes - not face time.