Employers in the USA often expect job applicants to bring more than just theoretical AI knowledge to the table - they want people who can actually deliver results. When I take a closer look at Ai training program like H2K Infosys, it's pretty clear that hiring managers are after candidates who can actually build, test, and put simple AI solutions into action, who can explain how their models make decisions, and can collaborate with the likes of data teams, QA folk and business stakeholders in a real-world project context
Programming: You'll need to know how to write with Python, do some basic SQL, and handle version control with Git or on Github
Core ML: being able to handle data front-end, model training, model evaluation and chasing down whether you can squeeze any more out of it
Tools: experience with scikit-learn, TensorFlow or PyTorch; plus the basics of the cloud (AWS, Azure or GCP)
Deployment: getting models out there via simple APIs, keeping an eye on them and documenting the whole process
Soft skills: you'll need to be able to break down a problem into something manageable, communicate that to others and collaborate with people from other teams
The ones who get the job tend to be the ones who can turn abstract AI concepts into working solutions that fit in with the real-world workflows of a business