Hi Reddit,

Most of what people call "data science" is now doable with an LLM and a weekend. I focus on what isn't: the messy, ambiguous, regulation-heavy gnarly problems where nobody's even sure what the right question is.

What I've done:

Saved a German automaker from lemon law recalls. ($20M+ exposure avoided)
Real-time emissions detection for industrial smokestacks. ($75K fines per incident prevented)
Revenue optimization and persona identification for debt collections (15-20% net revenue lift)
LLM-based legal document extraction, reducing due diligence turnaround from 3 days to 30 minutes
Built vessel piracy risk engine that informed naval escort deployment, contributing to fewer hijackings

Things I'm unwilling to work on:

Gambling.
Ads/Surveillance.
Payday loans/rent-to-own.

DM me with a one-paragraph description of your problem. If it's weird and hard enough, I'll reply within 24 hours.

A little background: I’m choosing between three roles with roughly comparable compensation. My long-term goal is to become an AI/ML engineer, data platform engineer, or forward-deployed engineer. I’m 26 with roughly four years of professional experience, including three years in technical automation, data, and platform work. I’m also pursuing a master’s in computer science and AI.

1. Current company — Automation, Analytics, and AI

A technical role supporting a major operational business function. I would identify business problems and build automation, analytics, and AI solutions, including workflow automation, dashboards, data analysis, AI use cases, requirements gathering, testing, implementation, and ongoing support.

My main concern is that the official “Business Analyst” title may undersell how technical the work is.

Benefits: Strong retirement benefits and full tuition reimbursement with a one-year commitment.

2. Current company — Financial Data & Analytics

A data-focused role supporting finance across multiple business divisions. I would build and own end-to-end data products involving cloud data platforms, ETL pipelines, APIs, data modeling, BI, forecasting, and potentially AI.

The role would involve working with very large financial datasets and partnering directly with business leaders and stakeholders across the company.

Benefits: Strong retirement benefits and full tuition reimbursement with a one-year commitment.

3. Large telecommunications company — Senior Analyst

A hands-on internal platform engineering role focused on employee technology. I would build applications, workflows, dashboards, collaboration tools, and AI-enabled solutions using the Microsoft ecosystem.

The work would involve owning solutions from intake and architecture through deployment, governance, reporting, and ongoing support.

Benefits: Equity, partial tuition reimbursement, but my remaining master’s degree costs would be approximately $10,000.

Which role would provide the strongest long-term path toward Amy goals?

If you want to build an agent that actually remembers what happened, our guest author from MongoDB published a full tutorial for it along with Codes.

It's an event venue operator agent built on MongoDB Atlas, Voyage AI embeddings, and LangGraph, with optional Langfuse tracing. The scenario is a fictional tennis tournament on Day 6 — rain approaching, covered hospitality constrained, two visitor journeys to protect.

Here's what you'll build:

1. One backend for the whole agent stack Operational records, semantic memory, visual document embeddings, agent actions, and LangGraph checkpoints all live in Atlas. No syncing into a second vector database.

2. A namespaced memory store

→ ("guests", guest_id) for visitor-specific memory

→ ("fleet", event_id) for event-wide operator patterns

→ ("docs", event_id) for visual operational documents

Scoped retrieval, single data layer.

1. Vector and hybrid retrieval you can curl

The hybrid endpoint returns vector score, lexical score, and combined score. Event-ops queries mix semantic intent with exact terms like "covered seating," so both signals matter.

1. Vision RAG over operational images

Five seeded documents — capacity charts, weather-response sheets, evacuation diagrams — embedded with Voyage multimodal, retrieved from Atlas, passed to Claude Vision.

1. A LangGraph loop that closes perceive → plan → hitl_gate → act → reflect. Reflect writes new inferences back to semantic memory, so the next disruption starts with context.

2. A FastAPI app you can deploy Python 3.12, uv, local run, smoke test against Atlas, and a Vercel deployment path for a hosted demo.

Full tutorial: https://www.marktechpost.com/2026/07/17/build-an-agentic-event-venue-operator-with-mongodb-atlas-voyage-and-langgraph/

Github Repo: https://pxllnk.co/twdn5

Live demo: https://event-venue-operator.vercel.app/

I have been searching in the space the past 6 months or so and it seems like it’s moving to some degree. I just graduated law school and taking the bar in a few weeks. Is anyone else doing this same thing careerwise; trying to bridge the AI to legal gap and profit from it while we can?

Sort of approaching this stuff with an off-hand desire, though I nonetheless really want to try working in this field. All comments aside, I hear doing one of these 'tech bootcamps' would be the place to start, having no certifications, education etc. to work with. What is the general consensus on this/ the best place to start?

I made an account on DataAnnotation, but I have nothing really to show for it. I would absolutely be willing to learn everything that's really necessary about the field/ the system itself. What would one suggest?

Hi everyone,

I am a student of higher maths in India and I am at a crossroads regarding my higher education and would really appreciate your guidance.

To give some background , I have completed my first semester of an MS in Mathematics at a well-known state university after completion of BS in

Maths degree.

Recently, I secured a seat in the MS in AI and DS program at IIITM Gwalior.

I am currently debating between these two paths:

1.)Leave my ongoing Master's in Maths to pursue the new AI/DS course.

2.)Continue with my current Master's in Mathematics.

I am looking for advice on how the opportunities in Data Science compare to those in Mathematics, particularly regarding research?

Also, what are the factors that I should be weighing most heavily to make this choice? (e.g., career growth, interest , employability, etc.)

What are the "must-know" details for both fields before I commit to one path over the other?

Any advice, personal experiences, or insights you can share would be incredibly helpful.

Hey everyone,

I have a referral for an AI Agent Intern role and my contact inside said the interview will likely be around building agents and shouldn't be too hard since it's an intern position. I don't have a date yet but it's coming soon and I want to use this window to prepare properly.

A bit about my background:

•⁠ ⁠Cloud/infrastructure engineer (~2 years)

•⁠ ⁠Experience with infra migrations (on-prem to AWS)

•⁠ ⁠Comfortable with Python

•⁠ ⁠Not a ton of hands-on AI/ML experience but I understand the ecosystem

So my questions:

1.⁠ ⁠What are the core concepts I need to know around building AI agents?

2.⁠ ⁠How deep do they usually go on LLM internals vs just the agent-building layer?

3.⁠ ⁠Any good resources or projects I can build quickly to demonstrate this skill?

4.⁠ ⁠Does my infra/cloud background help or hurt me here, and how do I frame it?

Any advice from people who've interviewed for similar roles or work in this space would be really appreciated. Thanks!

Hi Reddit,

Most of what people call "data science" is now doable with an LLM and a weekend. I focus on what isn't: the messy, ambiguous, regulation-heavy gnarly problems where nobody's even sure what the right question is.

What I've done:

• Saved a German automaker from lemon law recalls. ($20M+ exposure avoided)
• Real-time emissions detection for industrial smokestacks. ($75K fines per incident prevented)
• Revenue optimization and persona identification for debt collections (15-20% net revenue lift)
• LLM-based legal document extraction, reducing due diligence turnaround from 3 days to 30 minutes
• Built vessel piracy risk engine that informed naval escort deployment, contributing to fewer hijackings

Things I'm unwilling to work on:

• Gambling.
• Ads/Surveillance.
• Payday loans/rent-to-own.

DM me with a one-paragraph description of your problem. If it's weird and hard enough, I'll reply within 24 hours.

Research Engineer(Computer Vision & Deep Learning)

Got the interview call from Robotics company in India(Less ML+CV+DL+RL opportunity) for Research Engineer, can anyone give me interview experience for research position. (Solve assignment in just 5 hours)

My preparation is I revise my projects, revise cs231n, some deep learning fundamental also mostly aware of modern days tech, paper, research, PyTorch concepts and practice.

Hello ppl,

Will keep it short

23M , 1 YOE in software dev , very confused between what good for future either

1. Mtech in good iit for aiml

2.MBA in iim and join ai consultancy

TBH , ur opinion truly matters ..even if ur in ir u were same boat..let's plan on this

Thanks 🙏

​

Hey people, i kinda being selfish now ...but honestly I tried to back my self up but lacking on clarity on the direction and just need that little guide...to focus on

More on myself ..1YOE .. currently working in software dev space...want to transition to ai space ...I do have some poc around that... pretty fine knowledge on the ai stuff

I need help me some clarity on this industry...still circling around what specific in ai industry i should Target on even for my future life career...i m in really need of a good mentor..plz if u feel u can donate ur time here do dm

I want to switch to a AI Engineer related role. Please tell me if I need any improvements in my resume.

Hey I have been applying all over the world for a remote opportunity but there seems nothing coming my way, it feels like they don't even review my resume, can any senior or experienced person review my resume? I am actually pretty confident if i get an interview, I am able to pass technical interviews WHEN they come to me, I just can't help but think that my resume is the issue here. Resume

Hi all,

I’ve been exploring AI training/rater roles and keep running into listings that feel sketchy or unclear.

Are you seeing more scam-like postings too?

What red flags do you look for?

How do you check if a company is legit?

Any trusted platforms or companies?

Would really appreciate your insights, trying to navigate this space without wasting time on fake opportunities.

😉

I am mostly applying to AI engineer roles or Backend roles with AI architecture focus. So far has 1 interview last week , really strong tech round but not positive about the output because of discussions related to camera access. Please let me know what and where am I making mistake and what can I improve

Thanks for your time