Maintaining Your Career in an AI World

AI isn’t just changing code. It’s changing how careers are discovered, evaluated, and built.

Whether you’re a full-time engineer, contractor, or project-based technologist, your LinkedIn, GitHub, portfolio, and digital footprint are no longer just profiles — they’re machine-readable signals. Recruiters, hiring managers, and founders aren’t the only ones searching for you anymore. AI agents and LLMs are parsing your commits, ranking your skills, summarizing your experience, and recommending you (or not) before a human ever sees your name.

Every GitHub commit. Every README. Every LinkedIn update. Every conference talk. Every comment on a pull request. These are inputs into systems that summarize, categorize, and match professionals to roles, projects, and contracts.

But managing your career in an AI world is bigger than optimizing profiles.

It’s about capability, visibility, and community.

For mid-level engineers, architects, and technical leaders, this changes everything.

You will need to become more active than you are probably comfortable with — and more intentional about how your work, learning, and relationships are represented.

In this session, we’ll cover what that means in practical terms, including:

• How AI interprets your career narrative

• How to optimize GitHub and LinkedIn profiles for clarity

• Why personal websites and portfolios are becoming career infrastructure

• The difference between networking and being discoverable

• How in-person relationships compound digital visibility

• What continuous learning looks like when AI tools are part of your workflow

• How open source participation increases career optionality

• What to post, when, and how often

This is not about becoming a social media influencer.

But you likely can no longer afford to be a ghost, hiding in plain sight.

If you’re not intentionally shaping your career footprint — digital and human — something else is shaping it for you.