u/gaurav326913

Looking for guidance from experienced folks who have navigated the transition from traditional ML to the current GenAI/Agentic AI landscape.

I have around 9 years of experience in Data Science. Most of my career has been in traditional ML—classification, regression, recommendation systems, propensity models, etc. Over the last couple of years, I've been involved in a few GenAI initiatives, but mostly at the POC stage.

My current work is largely around calling LLM APIs for tasks like summarization, content generation, and similar use cases. While it's GenAI-related, I don't feel I'm building the kind of production-grade systems that many companies seem to be looking for.

I'm now planning a job switch and have been reviewing a lot of job descriptions. Almost every role seems to mention some combination of:

LLMs

RAG

Agents

MCP

AI System Design

LLMOps / MLOps

LangGraph

Evaluation & Monitoring

To be honest, I'm feeling a bit overwhelmed.

When I started my career, stepwise regression was still a thing. Then the industry moved toward ensembles, gradient boosting, deep learning, and now it feels like the expectation is that every Data Scientist should be able to design and deploy agentic AI systems.

For someone with my background:

What would you focus on first?

MLOps or Agentic AI?

System Design or hands-on frameworks?

Which resources actually helped you (courses, YouTube channels, books, projects)?

If you had to create a 3-6 month roadmap today, what would it look like?

I'm specifically looking for advice from people who were experienced Data Scientists and successfully made this transition, rather than generic beginner roadmaps.

Would appreciate hearing what worked for you and what you would do differently if starting again today.

Looking for guidance from experienced folks who have navigated the transition from traditional ML to the current GenAI/Agentic AI landscape.

I have around 9 years of experience in Data Science. Most of my career has been in traditional ML—classification, regression, recommendation systems, propensity models, etc. Over the last couple of years, I've been involved in a few GenAI initiatives, but mostly at the POC stage.

My current work is largely around calling LLM APIs for tasks like summarization, content generation, and similar use cases. While it's GenAI-related, I don't feel I'm building the kind of production-grade systems that many companies seem to be looking for.

I'm now planning a job switch and have been reviewing a lot of job descriptions. Almost every role seems to mention some combination of:

LLMs

RAG

Agents

MCP

AI System Design

LLMOps / MLOps

LangGraph

Evaluation & Monitoring

To be honest, I'm feeling a bit overwhelmed.

When I started my career, stepwise regression was still a thing. Then the industry moved toward ensembles, gradient boosting, deep learning, and now it feels like the expectation is that every Data Scientist should be able to design and deploy agentic AI systems.

For someone with my background:

What would you focus on first?

MLOps or Agentic AI?

System Design or hands-on frameworks?

Which resources actually helped you (courses, YouTube channels, books, projects)?

If you had to create a 3-6 month roadmap today, what would it look like?

I'm specifically looking for advice from people who were experienced Data Scientists and successfully made this transition, rather than generic beginner roadmaps.

Would appreciate hearing what worked for you and what you would do differently if starting again today.