Relevant CS career building advice

Recently, a CS undergraduate student (pre-final year) approached me with a question that’s on the minds of many undergraduates:

“What should I do to excel in my career, especially now that LLMs & AI seem to be changing everything? I’m worried about jobs disappearing.”

This is a valid concern. The rise of LLMs and generative AI is transforming the tech landscape, automating some tasks while creating new opportunities—though the nature and number of these opportunities are still evolving. Here’s my advice—updated for the LLM era—on how to build a resilient, future-proof career in computer science.

1. Understand the Evolving Tech Landscape

• Learn the basics, but stay curious about AI:
  Master core CS concepts (DSA, OOP, OS, DBMS, Networking), but also understand how LLMs and generative AI are being integrated into products and workflows.
• Explore new domains:
  LLMs are not just for chatbots—they’re used in code generation, data analysis, content creation, and more. Stay updated on how different fields (healthcare, finance, education, etc.) are adopting AI.

2. Build a Strong Foundation—With an AI Twist

• Core skills still matter:
  Algorithms, data structures, and system design are the backbone of any tech role.
• Add AI/ML fundamentals:
  Take introductory courses in machine learning, NLP, and deep learning. Understand how LLMs work at a high level (transformers, embeddings, prompt engineering).
• Learn to use LLMs as tools:
  Practice using APIs like OpenAI, Meta Llama, or open-source models to solve real problems.

3. Project-Based Learning: Build with LLMs

• Work on projects that leverage LLMs:
  Instead of a generic to-do app, try building a smart assistant, a code review bot, or a content summarizer using LLM APIs.
• Showcase your ability to integrate AI:
  Employers value candidates who can use LLMs to automate tasks, enhance user experience, or create new products.
• Document your process:
  Write about your projects, challenges, and learnings—especially how you used LLMs creatively.

4. Internships and Real-World Experience

• Target companies and teams working with AI:
  Look for internships where you can contribute to or learn from projects involving LLMs, data pipelines, or AI-powered products.
• Be proactive:
  If your internship isn’t AI-focused, find ways to suggest or prototype LLM integrations for existing workflows.

5. Resume and LinkedIn Optimization for the AI Era

• Highlight AI/LLM experience:
  List projects, hackathons, or coursework involving LLMs, prompt engineering, or AI APIs.
• Show adaptability:
  Emphasize your ability to learn new tools and adapt to fast-changing tech.

6. Technical Interview Preparation: Beyond DSA

• DSA is still important, but…
  Some interviews now include questions on AI concepts, prompt design, or even using LLMs to solve problems.
• Practice with AI tools:
  Use LLMs to help you debug, generate code, or explain concepts as part of your prep—but don’t rely on them blindly.

7. Soft Skills and Communication in the Age of AI

• Human skills are more valuable than ever:
  LLMs can generate text, but they can’t replace empathy, leadership, or creative problem-solving.
• Learn to collaborate with AI:
  Treat LLMs as teammates—know when to trust their output and when to question it.

8. Networking and Mentorship

• Connect with AI practitioners:
  Join AI/ML communities, attend webinars, and participate in hackathons focused on generative AI.
• Find mentors who understand the new landscape:
  Seek guidance from professionals who are actively working with LLMs.

9. Continuous Learning: Stay Ahead of the Curve

• Follow AI trends:
  Subscribe to newsletters, blogs, and podcasts about LLMs and generative AI.
• Experiment with new tools:
  Try out the latest open-source models, prompt libraries, and AI platforms.

10. Explore Career Pathways Beyond Coding

• LLMs are creating new roles:
  Consider careers in prompt engineering, AI product management, AI ethics, or technical writing for AI products.
• Interdisciplinary skills are in demand:
  Combine your CS knowledge with domain expertise (e.g., healthcare + AI).

11. Dealing with Rejection and Exploring options

• The landscape is competitive and changing:
  Not every application will succeed, and that’s okay. Use feedback to improve and adapt.
• Embrace lifelong learning:
  The best way to future-proof your career is to keep learning and evolving.
• AI is borderless:
  Many AI/LLM projects are open-source and global. Contribute to international projects and consider remote roles.

12. Work-Life Balance and Mental Health

• Don’t let AI hype overwhelm you:
  Focus on your growth, not just the headlines. Take breaks, pursue hobbies, and maintain a healthy balance.

Final Thoughts

The rise of LLMs is not the end of tech jobs—it’s the beginning of a new era.
Those who learn to work with AI, adapt quickly, and focus on human strengths will thrive.
If you’re a student today, you have a unique opportunity to shape the future. Start building, keep learning, and don’t be afraid to ask questions—just like the student who inspired this post.

At this point in your career friend, you’ve have selected your domain: CS Tech. I encourage and challenge you to have a growth mindset and break boundaries. If I can do it, you can too.

No one has ever started untill they did.

If you have more questions or want to discuss your career path, feel free to reach out!