Mastering GENAI Interviews: Insights, Challenges, and Real-World Success

"We continue to accelerate our leadership in Generative AI, which we believe is the most transformative technology of the next decade, delivering $3 billion in new bookings for the year,” said Julie Sweet, Chair and CEO of Accenture. This statement highlights the significant strides Accenture has made in leveraging Generative AI (GENAI), positioning itself as a key player in shaping the future of artificial intelligence. Accenture's market leadership in GENAI is driving its success, with the company on track to meet its yearly targets. After predicting a base salary of ₹9 lakhs for entry-level positions in GENAI, I foresee multinational IT companies, led by Accenture, offering starting salaries of ₹12 lakhs, potentially rising to ₹15 lakhs by the end of the year.

The Challenge of GENAI Interviews

As someone with 19 years of experience in hiring within the intelligence field, I've received numerous messages from students about the challenges they face in GENAI interviews. These students often express frustration about the open-ended questions posed by interviewers, which are dismissed when the solutions don't align with the interviewer's preconceived answer. This is a real challenge, especially when the recruiter lacks the necessary knowledge to assess the candidate's answer fairly.

The Nature of GENAI Interviews

Interviews for positions in GENAI—especially those offering salaries of ₹18 lakhs or more—are designed to assess the candidate's scenario planning and model design abilities. One crucial piece of advice for students: the interview should be driven by you. You must take control by seeking clarifications, framing the problem, and demonstrating your understanding of the situation. How you choose to approach the problem speaks volumes about your diligence, knowledge, and interest.

For those who ask, "What is a good answer to an interview question in GENAI?" my advice is clear: focus on real industry use cases. Deploying a GENAI application or a machine learning (ML) system gives you invaluable experience. If you've never worked on an industry-level deployment in academia, you likely won’t have the insight needed to address the nuances of real-world application deployment.

Types of GENAI Interviews

It's important for students to quickly identify what type of interviewer they are facing within the first few minutes of the interview. Different companies have different focuses, and aligning yourself with the interviewer's perspective is critical. Here are three common types of GENAI interviewers:

1. The Devil is in the Details: Companies looking for lateral hires, such as those I've worked with, often assume that candidates are strong in foundational GENAI concepts and AI architectures, ranging from CNNs to quantum computing. For these roles, paying attention to details is critical. The interviewer may dive into advanced topics like vectorization, embeddings, or inference, and expect you to exhibit deep technical knowledge, rather than focusing on simpler algorithms like decision trees or CNNs. For roles with salaries above ₹18 lakhs, your ability to master advanced AI topics will be closely scrutinized.

2. Tree of Thought Thinking Ability: Amazon and other tech giants often look for candidates who can break down complex problems into simpler, more manageable components. This is where Chain of Thought and Tree of Thought prompt engineering techniques come into play. The interviewer is looking for deep thinkers who can methodically deconstruct a large open-ended problem into smaller, solvable steps, determining which components need machine learning and which do not. Your ability to show this structured thinking process is what sets you apart.

3. Model Designing Ability: Google emphasizes creative and expressive problem-solving skills. They seek candidates who can identify the problem, define metrics, debate data sources, and then deploy the right model—or combination of models—after evaluating whether retrieval-augmented generation (RAG) or other techniques are required. The ability to consistently approach problem-solving in a systematic way, regardless of changing methods, is what distinguishes a skilled machine learning engineer.

The Advantage of Core Engineering Students in GENAI

Contrary to common assumptions, core engineering students—those from fields like electrical, mechanical, and civil engineering—often outperform their computer science peers in problem-solving interviews. While they may be weaker in coding, their strong domain knowledge and practical problem-solving experience allow them to design models and solutions faster. Core engineering students who are also proficient in GENAI are highly sought after, with salaries of ₹20 lakhs and more being offered to those combining VLSI design with GENAI. My collaboration with the ECE department has demonstrated how such interdisciplinary knowledge can push students into high-paying roles.

Training vs. Real-World Application

In academia, there is a heavy focus on model training and fine-tuning, which is only one piece of the machine learning puzzle. Employers, however, are frustrated by graduates who can only demonstrate textbook knowledge. In the real world, the focus shifts to model deployment, where the ability to build production-grade tools and solve industry problems is what matters most. Companies are paying high salaries because they expect candidates to have experience deploying models, not just fine-tuning them in a controlled academic environment.

In production, the ability to serve and deploy models at scale is far more important than simply training them. Many candidates make the mistake of overemphasizing training performance, neglecting how the model will actually be used in practice. In a GENAI-driven world, employers value candidates who can take a model from concept to production, ensuring it operates effectively in real-world scenarios.

ML and GENAI Questions for Students

To better prepare for these interviews, students should focus on the following machine learning and GENAI questions:

1. How would you design a scalable machine learning model for real-time inference?
2. What are the key challenges in deploying a GENAI model in production?
3. Explain how Chain of Thought and Tree of Thought techniques can be applied to complex problem-solving in ML.
4. Describe how you would use embeddings for a search engine application.
5. How would you combine multiple models in a GENAI system to optimize for both accuracy and inference speed?
6. What considerations would you make when deciding whether to use RAG in a GENAI system?
7. How do you ensure that a model generalizes well beyond the training data in a real-world application?
8. Explain the importance of model interpretability in machine learning deployments.
9. What are the trade-offs between model accuracy and performance when serving models in production?

By addressing these questions and aligning their interview strategies with the type of interviewer they encounter, students can significantly improve their chances of landing high-paying GENAI roles. The future of GENAI is here, and those with the right skills and mindset will be at the forefront of this technological transformation.