🎙 From Thapar to Cisco: A Student Roadmap to Gen AI Careers

In this exciting episode, Lakshita Aggarwal, a third-year B.E. Computer Engineering student at Thapar Institute of Engineering and Technology and trainee at Thapar Summer School 2025, chats with Ankit Kumar, Software Engineer at Cisco and M.Tech student at IISc Bangalore.

Ankit takes us through his journey from Thapar (Class of 2020) to Philips Innovation Campus and now Cisco, where he builds scalable AI-powered applications and explores GenAI, LangChain, and autonomous agents that solve real-world challenges.

💡 From debugging to building production-ready GenAI systems, Ankit shares how mastering fundamentals, embracing Kaggle projects, and staying curious about the “why” behind algorithms gave him a strong foundation for advanced AI/ML research.

🎯 For students dreaming of roles in AI, ML, and software engineering, this episode is full of real talk, practical project advice, and a clear roadmap for building impactful careers.

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👤 Expert: Ankit Kumar
• Current Role: Software Engineer – Cisco
• Past: Research Engineer – Philips Innovation Campus
• Certifications: AI Foundations (Thinking Machines), Practical ML with TensorFlow (IIT Madras), Python for DS & ML Bootcamp, Design and Analysis of Algorithms (IIT Madras)
• Education: M.Tech (Data Science & Business Analytics), IISc Bangalore (ongoing) | B.E. (Computer Engineering), Thapar Institute of Engineering & Technology
• Location: Bengaluru, Karnataka, India
• LinkedIn: linkedin.com/in/erankit97
• Email: ankitwave10@gmail.com

🎙 Host: Lakshita Aggarwal
• Program: B.E. – Computer Engineering (3rd Year)
• Institute: Thapar Institute of Engineering and Technology
• Trainee: Thapar Summer School 2025
• Location: Punjab, India
• Email: laggarwal_be23@thapar.edu
• LinkedIn: linkedin.com/in/lakshita-aggarwal-0409la6050

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🚀 What you’ll learn:
• How to transition from a research internship to full-time GenAI/ML engineering roles
• Why debugging and deep algorithm knowledge matter more than just relying on AI tools
• The role of LangChain, LangGraph, and LangSmith in building production-ready LLM applications
• Why Kaggle projects and end-to-end mastery of XGBoost give students an edge
• Emerging areas in GenAI research—from robotics to diffusion models—that students should watch
• How to pick projects that solve real problems and impress interviewers
• The mindset, soft skills, and adaptability that hiring managers actually look for in freshers