Review for AppliedAI – A Fantastic Experience So Far! - AI Engineer AppliedAI Employee Review

They know how to create the Agentic AI wave They know how to get traction and buzz from other organisations They celebrate small wins Multinational Colleagues

- Although they know how to create and ride the Agentic AI wave, there have not been many successful workflows or case studies they can showcase. Makes one wonder if all the claimed “wins” are actually “wins” or just for show - some colleagues like to play the blame game instead of finding the root cause and collaborate to solve issues when things don’t work out well - Management have high expectations, like they run a world class MNC when the company is still a startup. The demand doesn’t match with what they are supplying to their organisation ecosystem - high and quick turnover rate, makes you wonder there must be something off with management

1- innovative Work Environment You’ll often work on cutting-edge technology, such as AI models, data pipelines, and state-of-the-art algorithms, fostering creativity and innovation. High Demand for Skills ML expertise is highly sought after, providing strong job security and competitive salaries. Impactful Projects The work can directly impact industries like healthcare, finance, and entertainment, allowing you to see tangible results and societal benefits. Continuous Learning Opportunities The field evolves rapidly, so you’ll be exposed to new techniques, tools, and research, keeping your skills sharp and up-to-date. Collaboration with Talented Professionals ML companies attract skilled engineers, researchers, and data scientists, creating opportunities to learn and grow alongside peers.

High Pressure and Expectations The fast-paced nature of the industry and the demand for innovation can lead to tight deadlines and high stress. Ambiguity in Problem-Solving ML problems often lack clear solutions, requiring significant time and effort to experiment and refine approaches. Data Dependency Quality and quantity of data are critical; working with incomplete, messy, or biased datasets can be frustrating and hinder progress. Ethical and Social Challenges Balancing the ethical implications of AI applications, such as privacy concerns and algorithmic bias, can be a significant responsibility. Specialized Skillset Over-specializing in ML might limit your career options if you ever want to transition to a different field.