AI Impact in Data Jobs

I am convinced of how fast AI can grow and pick up everything that we humans do and as we see already a lot of jobs are being automated, i was always at the end of the spectrum i would like to believe, never was in the middle of anything. Either do so well or leave it has been my policy

So thinking deeply on this subject i came up with the following to keep my self relevant

How to keep ourselves relevant : The safest place is to stand at intersection of ai tools, strong data fundamentals and domain storytelling as all low value data work will vanish.

• **Data volume + Model Ops **: Pipelines, governance , and LLMOps become board level topics, demand for engineers who can keep models healthy in production is increasing. I read that there was 40% increase in these job posting on linkedin
• **Software 2.0 : **Andrej Karpathy in his recent speech talked about Software 2.0 and that is currently mainstream in AI product teams. Speaking in english to code is the current thing, and context based LLMs.
• New AI Native Roles : Gen AI Engineer , AI Product Manager ( Which i aspire to be) , Data ethicist, AI Risk Leader ( I can be here too as i am doing this already)
• **Continuous Learning : **Hiring based on knowledge rather than degree or institutions matter most.

How to stay relevant and well paid?

• **Double down on foundations **: Learn to code nevertheless you may not really code
• Be hands on : Add an AI Layer. Learn prompt engineering patterns, learn to train LLM, know at least one vector DB . Build personal projects
• Master ML Ops /LLMOps basics : Kubernetes, docker , CI/CD, model monitoring. This is the new DevOps
• **Learn to collaborate : **Collaborate, negotiate , network , mentor. This LLMs can never do soon :)

Nikhil Kamath’s article on times of india i read said below and i thought this made much sense : only those who “continuously adapt and reskill” will surf the new wave of AI employment.