Hindi AI Quality Assurance Tester

As a Hindi AI Quality Assurance Tester at Welo Data, your responsibilities will include reviewing and analyzing real-world data to ensure high standards of quality for AI models that utilize the Hindi language. You'll work closely with AI developers, providing feedback and insights on language nuances to enhance AI performance. Additionally, you'll utilize QA tools such as Jira to track your findings and collaborate with your team in the Portland lab.

To qualify for the Hindi AI Quality Assurance Tester position at Welo Data, candidates must have a four-year university degree in a relevant field, such as computational linguistics, digital marketing, or information technologies. Moreover, a linguistic competency in Hindi at CEFR C2 and English at CEFR C1 levels is essential. Experience with AI models, as well as familiarity with quality assurance tools, will also be beneficial.

While not mandatory, preferred skills for the Hindi AI Quality Assurance Tester position at Welo Data include basic knowledge of AI data structures and large language models, as well as an understanding of Agile work practices. Demonstrating a deep interest and knowledge in AI developments will also set you apart as a candidate for this role.

Hindi AI Quality Assurance Testers at Welo Data can look forward to a comprehensive benefits package, which includes paid sick time, access to telemedicine, and insurance options such as accident and critical illness coverage. Following eligibility requirements, employees will also receive medical, dental, vision insurance, and a 401(k) retirement plan, promoting not only their health but their financial well-being.

The Hindi AI Quality Assurance Tester role at Welo Data is a full-time position requiring 40 hours of work per week. All testers must work on-site at our Portland testing lab, which fosters collaboration and teamwork among colleagues. This structured environment ensures that you have the support you need to excel in your role.

In your response, highlight any specific projects where you performed quality assurance tasks, including data labeling or testing AI outputs. Mention any QA tools you've used, such as Jira, and explain how your experience has prepared you for the role at Welo Data.

Discuss the processes you follow to evaluate the quality of AI outputs, focusing on linguistic accuracy and cultural relevance. Provide examples of how you’ve identified issues in previous projects and your approach to correcting them.

Share any direct experience you have with Agile methodologies, including your role in Agile teams. Discuss how Agile practices have improved your collaboration and efficiency in past projects, particularly in relation to quality assurance.

Mention specific QA tools you’ve used, such as Jira or equivalent software. Provide examples of how you've employed these tools to track issues, communicate with development teams, and improve workflow efficiency.

Express your enthusiasm for Welo Data’s mission and values, focusing on their work in AI and data transformation. Relate your career goals to the opportunities for growth and learning that Welo Data offers.

Discuss strategies you’ve implemented when working under pressure, such as prioritizing tasks, collaborating with team members, and maintaining a systematic approach to quality assurance. Share any experiences where you successfully met deadlines without compromising on quality.

Share how you keep yourself updated with the latest developments in AI. Mention attending workshops, following industry blogs, or contributing to online forums. Explain how continuous learning enriches your skills as a QA tester.

Provide a specific example illustrating your problem-solving skills. Detail the issue you faced, the steps you took to diagnose the problem, and how you resolved it, emphasizing collaboration and communication.

Discuss your methods for task prioritization, such as assessing the impact and severity of issues found. Explain how you communicate priorities to your team and ensure seamless progress within projects.

Consider metrics like accuracy, user feedback, and efficiency improvements. Discuss how you would set benchmarks and goals, as well as how you would gather data to analyze the effectiveness of QA processes.