RWS team spotlight

1. How long have you been working with RWS on TrainAI projects?

I have been working on TrainAI projects for almost two years, since joining RWS in 2024. During this time, I’ve had the opportunity to contribute across different TrainAI projects.

2. How long have you been working in AI data annotation, rating, or training?

I’ve been working in AI data annotation, rating, and training since joining RWS in 2024. So, it’s been about two years now. I started in evaluation, later moving into auditing and quality roles, and also had the opportunity to work on pilot projects.

3. What was your path into AI data related work?

My path into AI data work grew naturally from my background in content development, research, and analysis. I’ve worked on various types of content – technical, academic, HR and psychology-related – all of which required evaluating information, understanding user intent, and communicating clearly. Collaborating with different clients and projects further strengthened my analytical, research and communication skills. TrainAI projects at RWS made the transition into AI space a natural next step.

4. What do you like the most about working on AI related tasks?

What I like most about working on AI-related tasks is the variety involved. Across TrainAI projects, I’ve worked on evaluation, auditing, prompt creation and other quality focused tasks, and each type of work brings a different learning experience. This variety keeps the work engaging and allows me to continuously develop new skills. 

I also value the role human judgement plays in improving AI outputs. Much of the work involves carefully reviewing responses and ensuring quality and safety. It draws on human strengths such as analytical thinking, attention to detail and communication skills, and I find it rewarding to contribute to outcomes shaped through collaboration between people and technology.

5. And what do you find the most challenging about it?

One of the most challenging aspects of AI related work is the continuous need to stay adaptable, as tasks and guidelines can change quickly. It also requires careful judgement, interpretation, and attention to detail since consistency and accuracy are critical. These challenges, however, make the work interesting and rewarding as they encourage continuous learning and help develop a deeper understanding of AI.

6. Tell us a little bit more about your background. Where are you from, where are you located, any interesting facts you would like to share?

I’m originally from India and currently based in North Carolina, USA, where I work remotely at home. My academic background includes a bachelor’s degree in engineering, an MBA in Human Resources, and a master's degree in organizational psychology. 

Before moving into AI-related work, I worked across HR, content development, research, and quality-focused roles – experiences that helped me develop strong analytical and communication skills. I’m also trilingual and fluent in English, Telugu and Hindi. 

My professional journey has taken me across different domains and roles, and one thing I’ve consistently enjoyed is learning, adapting, and growing with new opportunities along the way.

7. Working so closely with AI outputs, what changes or trends are you observing in the AI and machine learning industry?

One trend I’ve noticed is that human feedback still plays a very important role, even as AI systems become more capable. Human reviewers are needed to make sure responses are accurate, safe, and genuinely useful for users. 

The industry also feels very fast moving. The work environment doesn’t stay static – guidelines, expectations, and model behavior keep evolving rapidly showing how quickly the AI and machine learning space is growing.

8. What do you like the most about working with RWS?

What I like most about working with RWS is the opportunity to grow while working on a variety of AI projects. Over the past two years, I’ve had the chance to take on different roles – from evaluation and auditing to transcription auditing – and contributing to important pilot projects. This has allowed me to expand my experience. 

I also appreciate the supportive work environment. My team and managers have been encouraging and open to giving me opportunities to try new and different projects. The flexibility of working remotely and the chance to contribute across multiple projects make the work both interesting and rewarding.

9. Has your behind-the-scenes work with TrainAI changed how you personally interact with or trust AI tools in your daily life?

Absolutely, working behind the scenes with TrainAI has definitely changed how I interact with AI tools. I am now more aware of the importance of clear prompts, verification, and human judgement when relying on AI. I’ve also developed a greater appreciation for all the effort that goes into improving model quality and safety, which has strengthened my trust in AI when it’s used responsibly.

10. What skills or traits do you think are the most important for someone to be successful in AI data annotation and rating?

To succeed in AI data annotation and rating, there are several skills and traits that make a real difference. My pick would be attention to detail, since even small mistakes could affect the model quality. Analytical skills are important for evaluating data consistently, and patience helps when tasks become repetitive or tricky. Communication skills matter too, because annotation and rating aren’t just about analyzing data; they’re also about sharing insights effectively to improve the model. Finally, adaptability is essential since, AI projects, tasks, and guidelines are always evolving.

11. Without violating any NDAs, what is the most surprising, amusing, or unexpected AI response you have ever had to evaluate?

I haven’t encountered anything I can share directly, but it’s always interesting to see how AI sometimes interprets prompts in unexpected ways. Sometimes the responses can sound like the AI has put more thought into them than necessary, which is both amusing and a reminder that human judgement is still essential.

12. How does it feel knowing your day-to-day tasks are directly shaping the safety, accuracy, and future of Artificial Intelligence?

Honestly, it feels good. Knowing that my everyday tasks actually make a difference is motivating.