r/AIMLDiscussion

I'm building an AI-powered learning platform for school students (classes 6-12). I have about 2 years of hands-on experience integrating LLMs into the product, but to take it to the next level I need input from people who've built agents, RAG pipelines, and large-scale AI systems in production.

Over the last 1-2 months I've reached out to 50-60 senior AI engineers on LinkedIn. A few replied and a couple said they'd help, but nothing stuck and most went quiet. I'm not blaming anyone. I know senior folks are busy and a cold message from a stranger is a hard sell.

So I'd love some guidance on:

1. Where do experienced AI engineers actually hang out or pick up projects (communities, Discords, forums, platforms)?
2. Is it better to start with a paid contractor/consultant, a part-time advisor, or go straight for a co-founder-type person?
3. How do you approach senior people so they take it seriously? What makes you reply to a cold message?
4. Has anyone been on either side of this (founder finding a senior AI person, or the engineer being approached)? What worked?

I'm happy to pay for time on a trial or consulting basis, and open to equity conversations for the right person. Any advice or pointers are appreciated, thanks!

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I've been learning about AI agents, LLMs, and backend integrations lately, and one concept that caught my attention is MCP (Model Context Protocol).

Think of MCP as a standard way for AI applications to connect with external tools, databases, APIs, and other systems.

For example, imagine building an AI agent that can:

- Query a PostgreSQL database

- Fetch customer details from an API

- Read documents and search internal knowledge

- Trigger actions through connected tools

Instead of creating a completely different integration for every AI application, MCP provides a common protocol for discovering and using tools.

What I find interesting is how MCP fits into the bigger AI ecosystem:

- LLMs handle reasoning and language.

- Function calling lets models request specific actions.

- MCP standardizes how applications connect to tools and resources.

- Frameworks like LangGraph help orchestrate complex agent workflows.

I'm exploring how to combine MCP with Python, FastAPI, PostgreSQL, and AI agents to build practical applications.

Question for developers: Have you used MCP in a real project yet? Is it making your integrations easier, or do you still prefer direct API integrations?

I'd love to hear about your experiences and lessons learned.

How are you guys staying relevant with junior engineers who come prepared with all these latest technologies around AI engineering, LLMs, RAGs, etc

I know these are buzzwords but these buzzwords are attracting investments, creating new opportunities and opening up new roles.

I am mostly exposed to backend and data engineering and use basic AI tools. Now, I see new roles coming up around AI engineering, AI strategy, AI governance and AI infrastructure.

How do you guys prepare for all this while managing your regular day job?

Are you taking courses, reading articles, building your own AI-related projects or learning on the job? How do you decide what is actually worth learning and what you can ignore?

I am a software engineer with extensive database (SQL Server, Oracle), ERP Programming, Data Engineering, Data Architecture, Data Warehousing and Business Intelligence skills. I am thinking of learning AI with the objective of making AI products, not using AI.

Are there any specific courses anyone can recommend?

Thanks a lot in advance!

I’m a college student learning ML in 2026. With AI evolving so quickly, is it still worth investing time in ML, or should students focus more on AI tools and software engineering?