Myth 1: You Need a PhD or Master's
One of the most persistent myths is that a career in AI is reserved for those with a PhD or at least a Master's degree. While advanced degrees are crucial for highly specialised research roles, like an AI Research Scientist, they are not a requirement
for the majority of AI jobs in the industry. For roles like Machine Learning Engineer or AI Engineer, companies are increasingly prioritising practical skills and a strong project portfolio over advanced degrees. A Google DeepMind engineer even stated that a PhD can be 'overkill' for an ML Engineer role, as these positions require practical skills like data engineering and DevOps, which aren't always the focus of doctoral programs. For most entry-level roles, a bachelor's degree in a relevant field like computer science, combined with demonstrable skills in Python, ML frameworks, and a solid GitHub portfolio, is often sufficient.
Myth 2: An AI Career Is Only for Coders
While programming is central to many AI roles, it's a misconception that you must be a hardcore coder to work in the industry. The AI ecosystem is vast and requires a diverse range of talents. There is a growing demand for 'non-coding' roles that are essential for building and deploying AI products. These include AI Product Managers, who define the vision and strategy for AI solutions; AI Ethicists, who ensure models are fair and responsible; and Analytics Translators, who bridge the gap between technical teams and business stakeholders. Other accessible roles include Data Analyst with AI skills, AI Trainer, and Prompt Engineer, which focus on analytical thinking, communication, and domain expertise rather than complex programming. So, if your strengths lie in strategy, communication, or project management, there is still a place for you in the AI revolution.
Myth 3: You Get a Massive Salary from Day One
The stories of freshers landing astronomical salaries create a distorted picture of reality. While AI jobs are among the highest-paying in the tech sector, expecting a massive package right after graduation can be unrealistic. For 2026, the typical starting salary for a fresher in an AI or ML engineering role in India ranges from ₹6 lakh to ₹12 lakh per annum. Salaries for roles like Data Analyst with AI skills start lower, around ₹3.5 to ₹6 lakh. Factors like the city, the type of company (product vs. services), and the quality of your project portfolio significantly influence the final offer. The real financial upside in AI comes with experience and specialisation. While the starting salary is competitive, the true rewards come after gaining a few years of hands-on experience and keeping your skills updated.
Myth 4: AI Is Just Automating and Destroying Jobs
Headlines screaming about AI replacing jobs are everywhere, causing anxiety for those about to enter the workforce. The reality is more nuanced. While AI is automating certain repetitive tasks, particularly at the entry level, it is also creating new roles and restructuring existing ones. A recent study noted that while AI handles about 37% of entry-level tasks in India, 94% of HR leaders expect AI to generate entirely new roles. The focus is shifting from performing routine tasks to supervising AI systems and focusing on higher-value work that requires critical thinking, creativity, and strategic judgment. Rather than making jobs disappear, AI is raising the bar. Freshers are now expected to have skills in human-AI collaboration and be ready to solve complex problems from day one.
Myth 5: Entry-Level Means Working on Basic Models
Many freshers imagine their first AI job will involve mundane tasks like data annotation or working on simple, textbook models. While foundational work is part of any entry-level role, the nature of these roles is evolving rapidly. Due to the intense demand for talent, companies are hiring freshers for impactful roles like Generative AI Engineer, NLP Engineer, and MLOps Engineer. These roles often involve working with cutting-edge technologies like Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and sophisticated deployment pipelines. The key is to build practical skills in these specific, high-demand areas. Companies aren't just looking for a degree; they are looking for freshers who can build and operate systems, contribute to open-source projects, and demonstrate a genuine understanding of how to apply AI to real-world problems from the start.
