Hiring managers in entry-level AI roles look for a balance of strong fundamentals and practical problem-solving skills. In my experience working with early-career candidates, including those trained through H2K Infosys and similar AI and machine learning courses, the focus is on whether you can apply theory to real data and real systems, not just pass exams.
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Programming: Python proficiency, basic data structures, and libraries like NumPy, pandas, and scikit-learn
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ML fundamentals: Understanding supervised vs. unsupervised learning, model evaluation, and overfitting
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Data skills: Cleaning, visualization, and feature engineering
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Tools: Git, Jupyter, and exposure to cloud platforms such as AWS or Azure
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Soft skills: Clear communication and the ability to explain results to non-technical teams
Well-documented projects that solve real problems usually stand out more than certificates alone.