Pros
Salaries were credited on time. There was also exposure to a variety of AI and LLM related projects, which could benefit early career professionals looking to gain broad experience.
Cons
The work environment was often chaotic and extremely high-pressure. KPIs were unrealistic, and employees were expected to meet demanding performance standards with minimal training, even after being shifted to completely different projects. Raising concerns about tools or workflows was routinely dismissed or blamed on the employee. There was little consistency across teams, and the appeals process for quality reviews rarely worked in time to make a difference. In some cases, employees were told their roles were being elevated, but salaries remained the same despite the added workload. Many ended up working 10–12 hour days for the same entry-level pay. Leaves were very difficult to get approved, even for health-related reasons. Instead of allowing time off, the company often asked employees to simply “work less” that day but still meet targets. If there were any technical issues, even completely out of your control, you were still expected to make up the hours later in the week. Overtime was paid, but people were sometimes guilt-tripped into agreeing to it. Firings happened without warning or proper process. Overall, there was no transparency, consistency, and basic support systems needed for long-term growth or stability.