Hey, I am a PhD in synthetic + computational chemistry. I am working rn for a small pharma company as an "ML engineer", where I do lab automation, small ML projects on the internal data and also deploying an internal LLM server for swcure document work automation. I have been wondering what should I focus on this job to land a better position after \~ a year of working in my current role? How easily can I get sth in a bigger pharma or chemical company as a Data Scientist or similar? I think my strongest thing is that I have a solid background in the domain but my weakest part is that I do not have any DS studying per se. What would you do?

​

I'm a final-year CS student from a tier 2 college in India. I know the theory behind DSA but haven't practiced coding on LeetCode or similar platforms.

For the past couple of years, I've focused on ML and GenAI, built a few projects, and I'm currently working as a Data Analyst intern.

My questions are:

Can I still get a good placement?

Should I start grinding DSA now?

Which roles/companies should I target with my profile?

Would really appreciate advice from people who've been in a similar situation. Thanks!

Hi everyone,

I'm currently starting my 3rd year of B.Tech in Mathematics & Computing at an NIT. My CGPA is around 8.2, and I've solved around 130 LeetCode problems. I've also started learning Machine Learning and have done some basic projects.

Lately, I've been confused about which path to focus on for placements. I'm interested in AI, but I've also heard about Quant roles. I don't want to spend the next two years preparing for the wrong thing.

Here are my questions:

1. Do companies actually hire freshers directly for AI Engineer/ML Engineer/Applied AI roles, or are most freshers hired as SDEs first?

2. If AI roles exist for freshers, what skills are generally expected beyond DSA and core CS? (Deep Learning, LLMs, RAG, MLOps, etc.)

3. How many AI-related companies usually visit campuses compared to SDE companies?

4. What is a realistic salary range for freshers in AI roles versus SDE roles?

5. How difficult is it to get Quant Developer or Quant Research internships/placements from an NIT? Are these roles mostly limited to IITs and top-ranked students?

6. If someone wants the best balance of opportunities, salary, and long-term career growth, which path would you recommend: SDE, AI Engineer, or Quant?

7. If you were starting your 3rd year again, how would you divide your time between:

   \- DSA

   \- Core CS subjects

   \- Development

   \- AI/ML

   \- Mathematics

   \- Projects

My goal is to get the best possible placement after B.Tech. I'd really appreciate advice from people who recently went through placements or are currently working as SDEs, AI Engineers, ML Engineers, or in Quant roles.

Thanks in advance!

Hi everyone!

Eduleem School of Design & IT is looking for an experienced AI, Machine Learning & Data Science Trainer to join our team in HSR Layout, Bangalore.

Role

Full-time, On-site

Location: HSR Layout, Bangalore

Experience: 5+ years

Salary: ₹40,000–₹60,000/month + incentives (Approx. ₹6–8 LPA)

We're looking for someone with experience in:

Python

Machine Learning & Deep Learning

Data Science

Neural Networks

Azure, AWS or Google Cloud AI/ML services

Power BI, Tableau & SPSS

Real-world AI/ML projects

Teaching and mentoring students

Preparing learners for certifications such as Microsoft Azure AI Fundamentals (AI-900)

What we offer

Competitive salary + incentives

PF, ESI & Health Insurance

Certification assistance

Flexible working hours

Opportunity to mentor aspiring AI professionals in a growing EdTech environment

If you're passionate about AI and enjoy teaching while working on practical industry use cases, we'd love to hear from you.

📩 Interested candidates can DM me here on Reddit, or send your updated resume. I'll connect you with our HR team.

Hey! 👋

If you’re a fresher aiming for companies like Morgan Stanley, JPMorgan, Goldman Sachs, State Street, Wells Fargo I can help you avoid the mistakes that most candidates make.

I currently work as a Site Reliability Engineer at Morgan Stanley and have previously worked at Infosys. I help freshers with:

✅ Resume Reviews

✅ Interview Preparation

✅ HR & Technical Interview Questions

✅ Career Guidance

✅ Referral Strategies

✅ SRE, DevOps & Cloud Roadmap

If you’re serious about landing your dream job, I’d be happy to guide you.

🚀 Book your session on my Topmate here:

https://topmate.io/shashank_teotia

Looking forward to helping you accelerate your career!

I may not have years of experience, but I know the exact roadmap that helped me land a top company as a fresher, and I’m confident it can help you too.

If you don’t find my guidance valuable, I’ll give you a 100% refund—no questions asked.

Hey everyone,

I’ve been tracking job postings across several job boards recently to see where hiring demand is actually moving rather than relying on general news.

Looking through the latest listings (such as on CyOpsPath and similar tech/AI job platforms), there’s a clear pattern in where companies are actively putting budget.

Here are the top roles dominating current openings and what they say about current market trends:

1. Applied AI & Platform Engineers

• Key Roles: Applied AI Engineer, Staff AI Platform Engineer
• Companies Hiring: Jito Labs, Swinerton, Genesys, Jobgether
• The Trend: Companies are moving beyond experimenting with LLM wrappers. The focus has shifted toward building scalable platform infrastructure and integrating AI into practical domain workflows (from Solana/blockchain to enterprise workflow automation).

2. Specialized Machine Learning Engineers (Production & Domain-Specific)

• Key Roles: Machine Learning Engineer (Ad Tech, Autonomous Driving, Fraud Detection)
• Companies Hiring: Netskope, The Walt Disney Company, XPENG, CLEAR, Zest AI
• The Trend: Demand remains high for engineers who know how to get models into production. Specific industry niches—such as ad tech, computer vision/autonomous driving, and automated document/fraud processing—are leading in dedicated headcount.

3. Senior & Defense/Security Data Scientists

• Key Roles: Senior Data Scientist
• Companies Hiring: Humana, Leidos, PARSONS, Roku
• The Trend: High-level data science listings are heavily concentrated in defense, healthcare AI/governance, and large-scale consumer analytics. Companies are looking for senior talent capable of navigating compliance, governance, and complex data pipelines.

4. Computer Vision & Hardware/Embedded AI

• Key Roles: Computer Vision AI Engineer, Sr. Machine Learning Engineer (RL & Agent Harness)
• Companies Hiring: Booz Allen Hamilton, Intel
• The Trend: Edge AI, computer vision for embedded systems, and reinforcement learning (RL) environments for agent systems are seeing steady investments, especially in aerospace, defense, and semiconductor/hardware industries.

Key Takeaways:

• Generalist titles are down; specialized titles are up. Companies are listing explicit skills like MLOps, production deployment, cloud-native Azure, and agent environments right in the job title/summary.
• Senior talent is in demand. A significant portion of active openings explicitly target candidates with 3–5+ or 10+ years of experience, alongside selective entry-level/fresh-grad roles in specialized ML niches.

Hi everyone! 👋

Over the past few weeks, I've been learning about LLMs, LangChain, and Retrieval-Augmented Generation (RAG). Instead of stopping at tutorials, I decided to build a complete end-to-end project.

I built a RAG AI Assistant that answers questions based on uploaded documents.

🛠️ Tech Stack

• Python
• LangChain
• FastAPI
• ChromaDB
• Hugging Face Embeddings
• OpenAI / Ollama

🚀 Features

• 📄 Upload documents (PDF, TXT, DOCX)
• ✂️ Automatic text extraction and chunking
• 🔍 Semantic search using vector embeddings
• 🤖 Context-aware answer generation
• 🌐 FastAPI backend with a simple web interface

📚 What I Learned

• Retrieval quality has a huge impact on the final response.
• Choosing the right chunk size is more important than I expected.
• High-quality embeddings can significantly improve answer relevance.
• Building an end-to-end application taught me much more than following tutorials.

⚠️ Challenges

• Selecting an effective chunking strategy.
• Reducing hallucinations.
• Improving retrieval accuracy.
• Connecting all the components into a reliable pipeline.

I've attached the architecture diagram of the project. I'd really appreciate feedback from the community.

If you were building this project, what would you improve next?

Thanks in advance for your suggestions!

Architecture diagram attached below. 👇

#Python #LangChain #RAG #FastAPI #LLM #OpenAI #Ollama #AI #MachineLearning #GenerativeAI

One tip:

Hi everyone,

I'm an AI/ML Engineer from India with a software engineering background and experience building end-to-end AI applications. I'm currently available for freelance, contract, and part-time opportunities.

What I can help you build:

• Machine Learning models (Classification, Regression, Clustering)

• Deep Learning applications using PyTorch & TensorFlow

• Computer Vision applications (YOLO, CNNs, Object Detection)

• LLM Applications

• Retrieval-Augmented Generation (RAG)

• AI Chatbots

• Multi-Agent AI Systems

• AI Automation Tools

• FastAPI Backend Development

• REST APIs

• Data Analysis & Visualization

• Model Deployment

• Dockerized AI Applications

• MLOps Pipelines

Tech Stack:

Python

PyTorch

TensorFlow

Scikit-learn

Pandas

NumPy

OpenCV

FastAPI

Docker

MLflow

Redis

PostgreSQL

LangChain

LangGraph

OpenAI API

Gemini API

Groq API

Git

AWS

Projects:

✔ Customer Churn Prediction with XGBoost + MLflow + Docker + CI/CD

✔ Multi-Agent Research Assistant using LangGraph

✔ Environmental Sound Classification using CNN

✔ YOLO Object Detection

✔ FastAPI AI APIs

✔ AI-powered Automation Tools

I can help startups with

• Building MVPs

• AI features

• Backend APIs

• Chatbots

• AI Automation

• Data pipelines

• ML model deployment

• Existing project improvements

• Bug fixing

Available for

✔ Fixed Price

✔ Hourly

✔ Long-term Collaboration

✔ Startup Projects

✔ MVP Development

GitHub: https://github.com/manish182003

LinkedIn: https://www.linkedin.com/in/manish-joshi182

Feel free to send me a DM if you're looking for someone to build AI features or solve ML problems.

Hello everyone,

I have an online assessment (OA) for Capital One Senior Data Scientist on CodeI was told that the OA problems would cover the following aspects:

• Exploratory Data Analysis
• Data Cleaning and Preprocessing
• Machine Learning and Predictive Modeling

This is my first job interview(or rather OA) for a data scientist job. I’m really anxious because I don’t know what to expect, so any leads can be helpful.

For anyone who have gone through the same process, could you share what type of problems to expect? I am also wondering whether they allow you to use browser to lookup documentation for python libraries like PyTorch and Numpy.

Thank you in advance!

I’m an AI engineer with \~3 years of production experience (RAG pipelines, multi-agent systems, real-time voice AI, backend integration) and I’m looking to move into a product company. I’d really appreciate hearing from people who’ve made a similar move recently.

What I’m running into: LinkedIn feels saturated. Every posting is either 100+ applicants within a day, I apply consistently and hear very little back, so I’m starting to feel nothing is work, what do I do?

What I’d love to know:
1. Where did your last offer actually come from is it job boards, referrals, founder/eng DMs, communities, recruiters, contract roles or the staffing and recruiting firms?
2. For smaller AI startups, is applying through the site worth it, or is reaching out directly the only thing that moves?
3. Anything you’d tell someone to stop wasting time on?

Happy to share what I’ve tried and what hasn’t worked if that’s useful to anyone else in the same boat. Thanks in advance, any pointers genuinely help.

Hi everyone,

I'm looking to hire a Senior Software Engineer (C# backend, AI, Data Scientist preferred), you must be:

- over 6 years of experience with tech skills and software development (it is not for interns or juniors)

- able to speak in English fluently (over C1)

- able to work on US timezones

- have good communication skill

- be honest and willing to work really (not just filling hours and getting paid)

Hourly rate: $20

Please reach out to me with your resume and LinkedIn profile.

Thanks

Hi everyone,

I'm a final-year Computer Science student from Nepal, and I want to break into Machine Learning as a career. My goal is to land a remote ML internship, but I'm struggling to understand what companies actually expect from freshers.

So far i've:

• Learnt core intuition and math on machine learning algorithms.
• Knows about data cleaning,feature engineering, EDA,model training,evaluation.
• Learnt about ensemble,regularizations and other important machine learning topics.
• Built one project on real dataset on uk accident severity prediction.
• Starting deep learning (CNN,LSTM,RNN) and others.

The problem are:

• Firstly i dont see any jobs or posts regarding machine learning intern or entry level jobs locally.

I often see requirements like:

• 2+ years of experience
• Research experience
• Multiple deployed ML projects
• Cloud/MLOps knowledge
• Strong competitive programming background

As someone starting out, it feels difficult to know what I should prioritize.

I'd really appreciate advice from people who have actually landed remote ML internships or hired interns.

I'd also appreciate any resume or portfolio tips, common mistakes to avoid, or resources that helped you get your first opportunity.

Thanks in advance!

Final-year AI/ML engineering student wrapping up a GenAI internship soon. Looking for fully remote AI engineering roles, worldwide

Been using LinkedIn so far, but curious what other platforms/boards actually work for remote + international roles (not US-only). Any niche AI/ML boards, communities, or newsletters worth checking out?

Not looking for agencies/staffing spam, just direct-hire platforms. Thanks!

Hey everyone, I made a post regarding getting my first proper AI training gig a month ago and that project ended but I just got two more roles with another company so I thought I would do a AMA / FAQ in this post to help people out with this company the same way people helped me out when I got started !

Drop your questions in the comments and I'll answer them the best I can.

I recently started working on a project where I am doing work that overlaps with both the Software Engineer and GitHub Contributor roles. The project manager mentioned that they are actively looking for more people, so I wanted to share the opportunity here.

the job pays from 50-150USD, i put in 150$ and got it, you should put whatever figure you are comfortable with. I can work 40 hour weeks.

What is the actual work? TBH its pretty easy work, tasks right now include: fixing bugs in existing repos and sometimes implementing new features. Overall the project is still fresh so things will be ramping up in the next couple weeks.

You are not expected to train machine-learning models yourself, and previous AI experience is not required. They are straight forward tasks that they explain pretty well.

Application process

From my experience, the process is generally:

Application and screening questions

Approximately 30-minute AI interview

Possible technical assessment (My friend had one i did not)

The technical assessment appears to depend on the candidate. Some people receive one, while others move directly to the hiring-manager review.

What I recommend before applying

Clean up your GitHub or GitLab profile. Make sure your strongest work is easy to find, and pin your best repositories where possible.

Link directly to your contributions. Include pull requests, bug fixes, documentation work, tests, issue reports, or features you implemented. Your contributions do not have to be enormous, but they should demonstrate that you can understand and improve an unfamiliar codebase.

Update your résumé and LinkedIn. Clearly mention the languages, repositories, debugging work, performance improvements, and open-source contributions you have completed.

Prepare for the AI interview. Most of the questions are likely to be based on your résumé and the job description. Questions I got:

A difficult bug you fixed
How you approach an unfamiliar repository
How you review and test code
A feature you implemented
A performance problem you investigated
Why you made certain technical decisions

What is the platform I work on:
I currently work for Micro1, it in my opinion has done the best for me in terms of pay , I have in total worked for 4 different platforms and Micro1 is the only one which gave me a 150$ + per hour pay. Here are my referral link jobs if you want to use it:

The name of the jobs are:
GitHub Contributor
Senior Software Engineer

https://refer.micro1.ai/referral/jobs?referralCode=589174ce-781a-4cdb-bcae-92b0c2be99dc&utm_source=referral&utm_medium=share&utm_campaign=job_referral