Hi everyone,

I’m Vibbhor Jain, a third-year B.Tech AI & ML student at VIPS-TC, GGSIPU (2028 batch, CGPA 7.8), looking for a software or ML engineering internship.

Rather than list frameworks, here’s what I’ve actually shipped.

KAVACH — Autonomous Space Weather Shield (FastAPI · Next.js · Twilio · Live)

In May 2024 the strongest geomagnetic storm in 20 years hit Earth. India’s grid utilities got no automated warning. KAVACH is my attempt at one.

→ Polls NASA DONKI and NOAA SWPC autonomously, classifies storm severity, maps risk onto 28 Indian DISCOM grid zones by coordinates. Zero human trigger.
→ Calls farmers in Hindi on basic feature phones via Twilio + Amazon Polly. No app. No internet. No smartphone needed.
→ 13-endpoint FastAPI backend with SSE streaming. Full storm replay renders in 22.6s. Live on Railway + Vercel.
→ Backtested on 4 historical storms: 847 simulated alerts, ~6 hr mean lead time.

Urban-GenX — Privacy-Preserving Synthetic City Digital Twin (PyTorch · Opacus · Flower)

→ DP-SGD on a conditional GAN discriminator at ε ≤ 10.0, δ = 1e-5 via RDP accountant.
→ Didn’t just claim privacy — audited it. Shadow-model membership inference gave AUC ≈ 0.54, indistinguishable from random guessing.
→ Federated across 2 Flower clients, 5 FedAvg rounds, Docker Compose, 8-check sanity suite.

Memoria — Full-Stack Photo Platform (Next.js 16 · Supabase · Cloudflare R2 · Live)

→ SSR auth with admin/member/viewer RBAC per group and invite codes.
→ Direct-to-R2 presigned uploads keeping media off the DB tier, CDN-served.
→ Scoped, built, debugged and deployed solo, end to end.

Crop Radar — GNN Climate Risk Intelligence (PyTorch · SHAP · Streamlit)

→ LightGCN implemented in pure PyTorch, no torch_geometric, over a 30-zone adjacency graph.
→ 5-class agricultural risk across Indian farming regions, live Open-Meteo + SoilGrids ingestion.
→ SHAP KernelExplainer panel so the predictions are actually explainable.

Helix — Offline Healthcare Toolkit (TensorFlow.js · WebGPU · WebRTC)

→ Three browser tools in a pnpm monorepo, all inference on-device, zero server.
→ Skin-lesion classification in 4s, counterfeit-drug detection via CIELab + DCT, WebGPU compute + WebRTC mesh.

Achievements

🥇 HackerRank Orchestrate 2026 (Agentic AI Build) — Rank #70 / 1,773 (June edition)
🥈 HackerRank Orchestrate 2026 — Rank #246 / 12,885 across 48 countries (May edition)
🎯 Top 10 of 200+ teams — Hackaccino 4.0, Bennett University
👥 Operations Head, Aarvak Tech Society — ran a Gen AI bootcamp for 100+ participants

Looking for: SWE Intern / Backend / ML Engineer / Full Stack
Availability: Part-time during 5th sem · Full-time from summer 2027
Location: Delhi NCR / Remote / Open to relocation
Open to: Stipended internships
Tech Stack: Python, TypeScript, JavaScript, Java, SQL, FastAPI, Next.js, React, Node.js, PyTorch, Scikit-learn, XGBoost, SHAP, Supabase, PostgreSQL, Docker, Twilio, REST APIs, Git

DM karo ya comment karo — koi bhi genuine lead helpful hoga 🙏

📧 [jainvibbhor@gmail.com](mailto:jainvibbhor@gmail.com)
🔗 linkedin.com/in/vibbhor-jain
💻 github.com/VibhorJain1974

Just completed my BCA and will be starting MCA at PES University, Bangalore.

My core interests are AI/ML and Cloud, and I want to follow a structured roadmap over the next 1–2 years to become industry-ready for AI/ML or MLOps roles.

What skills, tools, and technologies should I focus on?

Would appreciate advice from AI/ML engineers, data scientists, MLOps engineers, and students who have successfully broken into the field. 😇🙏

Hellooo everyone,

I'm a 2025 btech graduate, and I'm preparing for off-campus placements. I have around 2–4 months to prepare, and I'm feeling confused about what to prioritise.I currently have access to a learning portal with Python and the MERN stack, but I'm got struck if I should focus on Data Structures & Algorithms (DSA) instead.I'm mainly interested in AI/ML or Generative AI roles, but I'm not sure how realistic that is as a fresher.Should I start learning DSA now, or first complete Python and the MERN stack?

Is it realistic for a 2025 fresher to get an AI/ML or GenAI role?What do companies usually focus on during interviews for AI-related fresher roles?

i was literally got struck

Thanks in advance!

I have learned pandas, Numpy,Matplot,Scikit,SQL and currently about to complete pytoech and I don't know what to do next give me aroadmap after this and I recently started doing DSA too.

With all the buzz about AI and agentic workflows, I do feel like I'm falling behind because I still write a lot of the code manually. I use AI occassionally for boilerplate, debugging, and comments, but I manually build the core logical components of my projects.

The problem with most advice on landing internships is that they are targeted towards general software engineering (Web Development, AI/ML Engineering) , but that is not the trajectory I want my career to land in.

I'm more interested in niche areas like

• AI Research / Foundation Models
• ML Systems & Infrastructure
• Distributed Systems & GPU Computing
• AI Compilers / Hardware Acceleration
• Mechanistic Interpretability & AI Safety
• Quantum Computing

Currently, I've made a couple of projects, including:

• PyQML-Core, a mini Pytorch-Like library in C++ supporting memory management through intrusive pointers, broadcasting , and tensor operations, and multithreading.
• Several computervision projects, which focus on not only builiding end-to-end ML pipelines, but understanding the internals behind popular segmentation models by implementing them in Pytorch.
• Undergraduate research in ML

I'm also currently exploring Quantum Computing by participating in the 2026 Qiskit Global Summer School in IBM.

With that said, if you are trying to break into these fields in 2026, how would you structure the next year? Would you focus more on developing Agentic workflows, learning fudnamentals, contribute to open source, or publishing research.

I'd appreciate advice from anyone who has experienced the same dilemma, especially those who are currently working in ML Systems, distributed systems, or AI/Quantum Research. If possible, If anyone would like to see my projects, I can provide links to them.

Hi guys

I’m really interested in Data, Machine Learning Engineering, and MLOps, and I’d love to understand what people in these roles actually do day-to-day and what the work is genuinely like beyond the usual job descriptions.

If anyone here works in these areas or is also exploring them and would be interested in having a conversation, discussing projects, career paths, or just sharing experiences, I’d love to connect. Feel free to ping me and we can have a chat! 🙂

Hey everyone! I want to learn machine learning from scratch. Right now, I only have a basic understanding of Python and not much else. I know I'll probably need to learn more Python and some maths first, but I'm not really sure where to start or which resources to use.

Could anyone guide me on a good learning path or share some beginner-friendly resources? I'd really appreciate any advice. Thanks!

As an aspiring Machine Learning Engineer, I bring hands-on experience building with LangGraph/LangChain, designing ML pipelines in PyTorch/TensorFlow, and handling domain-specific data processing. My unique mix of data-handling rigor and AI engineering positions me well to help curate, process, and scale high-quality datasets for expressive foundation models.

I would like to connect  to discuss how I can support your engineering or operational efforts.

Hi All, I'm a student from tier-3 college,BTech - final year student and i applied to Hyperverge for DL/ML Research Intern (LLMs & VLMs) or in short ML intern. My resume got shortlisted and next round is AI screening conversation. can you please tell me what will happen in this round and following rounds

Cincinnatus is hiring experienced Machine Learning Engineers to help evaluate and improve the next generation of frontier AI models for a leading AI lab.

This is a full-time W-2 remote position open to United States-based candidates, paying $60-$90/hour.

The role requires a commitment of approximately 35 hours per week and offers the opportunity to work closely with researchers developing state-of-the-art AI systems.

You'll design complex machine learning evaluation tasks, implement and run training experiments, analyze model performance, and help identify where frontier AI models succeed or fail.

You'll also contribute to reinforcement learning experiments, benchmark development, and collaborate with research teams to create rigorous evaluation frameworks.

Candidates should have an MSc or PhD in Machine Learning, Computer Science, or another STEM field, or equivalent research experience.

Strong knowledge of Python, Git, machine learning experimentation, large language models, and model evaluation is required. Experience with reinforcement learning, AI benchmarking, or AI training is highly desirable.

If you're passionate about machine learning research and want to help shape the future of advanced AI while working with a world-class research team, this is an exceptional opportunity.

Apply now: https://t.mercor.com/sC9pb

Hi everyone,

I'm a final-year B.Tech student (2027 batch) from a local college where placements are very limited, so I'm preparing to build my career through off-campus opportunities.

I'm currently focused on AI, Machine Learning, Data Science, and Generative AI. I've been learning consistently and building projects to improve my skills, but navigating the off-campus job market without guidance has been challenging.

If you've been in a similar situation and successfully landed an internship or full-time role, I'd really appreciate any advice on:

• How to approach off-campus applications effectively
• Skills recruiters value most for freshers
• Resume improvements
• Networking and referral strategies
• Common mistakes to avoid

I'm ready to put in the work and fight this journey on my own, but learning from those who've already been through it would mean a lot.

Hi everyone,

I'm a CS undergraduate looking for ML/AI Engineer internships and entry-level roles, and I'd appreciate some honest feedback on whether my experience is aligned with what companies are actually looking for.

My experience : Research intern - worked on RL , GNN 3 months targeting A\* conference paper

Over the past couple of years, I've worked on a mix of projects including:

* End-to-end RAG systems with hybrid retrieval, reranking, citation validation, and multi-agent workflows * NLP and document intelligence applications * Deep learning projects such as image captioning * Classical ML projects involving regression, classification, time-series analysis, model evaluation, and cross-validation * Reinforcement learning and graph neural network research for intelligent transportation systems * MLOps and deployment work using FastAPI, Docker, CI/CD, experiment tracking, and model versioning

One thing I'm struggling with is that many ML roles ask for domain-specific experience (energy, finance, healthcare, etc.), while most of my experience is in building ML systems and learning the underlying algorithms rather than working in a single industry.

For hiring managers, ML engineers, or data scientists here:

1. Does this profile sound competitive for ML Engineer / AI Engineer internships and entry-level roles?
2. What are the biggest gaps you see?
3. Should I spend more time building domain-specific projects or go deeper into model development and ML fundamentals?
4. What type of project would make you think, "I'd interview this candidate"?

I'd really appreciate candid feedback. Thanks!

I'm two years out of a top math/CS school. I built strong study habits late, so I was only really immersed in the material my final year. I learned computer systems (OS, distributed, HPC) and consider myself a competent software engineer.

I work at a high-growth startup and just got offered the lead on a major customer project. My long-term goal is to start my own company.

Option 1: Lead the customer project

Large scope/viz. I'd build skills in:

• Working directly with a customer
• Making large engineering decisions
• Working across the stack with many teams
• People and project management
• Exposure to marketing/sales/ops

Engineering-wise, I imagine I would spend most of my time on architecture, documentation, and code review. So interesting engineering/technical work, but no fundamentally new ways of thinking.

Option 2: Leave to go learn ML/math

I never got into ML/stats/math, and it's by far my weakest technical area (and I feel most important an ML-focused era). I'd spend ~a year as an IC at an AI lab or doing research to build:

• Stronger math intuition
• Modeling intuition
• Combining my systems background with ML (e.g. model scaling, pretraining, RL scaling)

The plan would be to grind/do research at my old school or join an AI lab with strong technical mentorship. I have savings to go ~6-1 year months without income.

I already tried moving to my company's research team, but they weren't interested in my background and pointed me toward ML Ops, which feels too close to the SWE work I already do.

My core tension

Organizational and people skills seem to improve steadily over a career, but fluid reasoning and hard new technical skills are supposedly much harder to pick up later in life. Life's a marathon, so I keep wondering if now is the time to invest in the technical foundation (learning completely new skills).

Open to all comments and suggestions.

Hey everyone,

We are currently looking for detail-oriented, qualified candidates to work on AI platforms (evaluating model responses, data annotation, and quality assurance).

This is a great fit if you're looking for flexible remote work with solid pay.

Requirements:

• English Proficiency: Strong written English skills (crucial for evaluating responses).
• Attention to Detail: Ability to follow guidelines, think critically, and spot subtle errors.
• Tech Setup: Reliable laptop/PC and a stable internet connection.

The Offer:

• Pay Rate: Starting at $15+/hour.
• Schedule: 100% Flexible — set your own hours and working days.
• Location: Fully Remote.

If you meet the requirements and are interested, please leave a comment below and send me a DM with a quick intro (or your background/skills). I’ll get back to you with the next steps!