Stage 1: Foundation (Undergraduate Years)

Focus on building a robust theoretical and practical base.

Core Coursework: Excel in Data Structures, Algorithms, Linear Algebra, and Probability & Statistics.
AI Specialization: Take advanced electives in Machine Learning, Natural Language Processing (NLP), and Computer Vision.
Practical Experience: Secure 1-2 internships in data science or software engineering. Contribute to research labs, like your current computer vision work.
Portfolio Projects: Continue building projects like your 3D Tic-Tac-Toe AI and Pokerbot to demonstrate practical skills.

Stage 2: Specialization (Years 1-4 Post-Grad)

Deepen your expertise and establish yourself in a specific AI domain.

Option A (Industry): Target roles like Machine Learning Engineer, Data Scientist, or AI Researcher at tech companies. Focus on deploying models at scale.
Option B (Academia/Advanced Degree): Pursue a Master's or Ph.D. in a specialized area like Reinforcement Learning or Generative Adversarial Networks (GANs) to target senior research roles.
Key Skills: Gain proficiency in cloud platforms (AWS, GCP, Azure), MLOps tools (Kubeflow, MLflow), and large-scale data processing (Spark).

Transition from individual contributor to a leader and innovator in the field.

Senior Roles: Grow into a Senior/Staff Machine Learning Engineer, a Tech Lead for an AI team, or a Principal Research Scientist.
Mentorship: Begin mentoring junior engineers and contributing to your company's technical strategy.
Industry Presence: Speak at conferences, publish papers or blog posts, and contribute to major open-source AI projects.
Potential Paths: AI Product Manager, Research Manager, or founding your own AI-focused startup.

"Generate a genai.html file that shows a potential career roadmap for me and has a flexbody at the bottom where I can add any prompts I used to create the page."