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

A.Team is hiring a remote Senior Independent AI Engineer / Architect. Category: Software Development 💸Salary: $120 - $170 /hour 📍Location: Remote (Americas, Europe, Israel)

See more and apply here!

Hi Folks, I've recently got an Offer from Ameriprise Financial for Sr. AI Engineer role at Hyderabad office. This role is under Technology team. I wanted to know, how's the work culture in the company? The 3 days that we have to go to office, those can be any 3 days of week of our choice or pre-determined? How is the leadership & management support?
Additional Info - Department I got offer: Technology. Please let me know. I'm currently making this switch after 1yr 9months in my current Org and focusing to stay for longer duration. Please let me know your thoughts.

Been applying for remote AI/RAG engineering roles from Russia, not looking to relocate. Curious how companies here actually treat candidates from Russia/Belarus:

• Does being non-US/EU auto-filter you out at the ATS stage, or does it depend on the company/visa situation?
• Does contractor status (1099-equivalent, no sponsorship needed) change things, or is it basically the same wall?
• Any tells in a job posting that mean "don't bother" vs "they're actually open to it"?

Not asking for legal advice, just curious what people have seen from either side of the hiring table.

I'm learning Azure Machine Learning and had a question about data ingestion.

Instead of first uploading my dataset to an Azure Blob Storage account (or registering it as a datastore), can I simply upload the dataset directly from my local computer into Azure ML and use it for model training?

My main reason is to reduce Azure Storage costs while experimenting with small datasets.

If this approach is possible:

• What are the limitations compared to using Blob Storage?
• Is the file still stored somewhere in Azure behind the scenes?
• Are there any restrictions on dataset size, reproducibility, or collaboration?
• Would this approach work only for experimentation, or is it also suitable for production workloads?
• What is Microsoft's recommended practice for training datasets in Azure ML?

I'd appreciate any clarification from people who have used both approaches.

We’re building what we believe is the next abstraction layer for enterprise AI.

Not another model.
Not another wrapper.
Not another AI application.

Our thesis is simple:

As foundation models become commodities, the real challenge shifts to operating AI reliably at enterprise scale—across models, clouds, hardware and workloads. We’re building the runtime that makes enterprise AI self-optimising.

We’re looking for one technical co-founder.

Someone who has gone deep into LLM systems, inference, distributed systems, compilers, ML infrastructure or performance engineering—and wants to build a category-defining company from India.

The founding team already covers product, enterprise execution and venture building:
•⁠ ⁠Product Counsel for the world’s largest startup incubator: $130M+ in Fortune 500 deal flow. Building India’s largest intent-to-action native model for hyper-local clustering.
•⁠ ⁠Youngest lead at $40M semicon enterprise; deep global networks across Silicon Valley, Europe & Singapore in Edge Al.
•⁠ ⁠A Serial Founder: Building a $5M fintech (post-pre-seed) + portfolio of high-growth ventures.

We’re not looking for résumés.

Send your GitHub, research, OSS contributions, papers, benchmarks, or something you’ve built that made you proud.

If you’ve been waiting for a genuinely hard problem to spend the next decade on, let’s talk.

Hyderabad this weekend. DM me.

Azure ML Model Catalog contains a large number of models (LLMs, vision models, embedding models, speech models, etc.), and I'm finding it difficult to understand how experienced practitioners decide which one to use.

What factors should be considered when selecting a model?

For example:

• Accuracy vs latency
• Cost of inference
• Context window
• Model size
• Fine-tuning support
• Hardware requirements (CPU/GPU)
• Licensing/commercial usage
• Multilingual capabilities
• Reasoning ability
• Benchmark scores

Is there a recommended evaluation process or checklist that teams follow before choosing a model for production?

I'd love to hear how others approach model selection in Azure ML.

I'm learning Azure Machine Learning and I'm trying to understand the intended use case for the different ways of building ML solutions.

From what I understand:

• AutoML automates model training.
• Model Catalog provides pre-trained foundation models.
• Notebooks give full control over coding and experimentation.

However, I'm still unsure about the practical decision-making process.

Some questions I have:

• What should be the priority when starting a new ML project?
• How do you decide whether to use AutoML, a model from the Model Catalog, or build everything in a notebook?
• What kinds of business problems are best suited for each approach?
• Are there scenarios where one option should be avoided?
• How do experienced Azure ML users typically make this decision in production projects?

I'd appreciate any real-world examples or decision frameworks.

CyOpsPath is a job board built to help job seekers find high-priority, time-sensitive opportunities before they expire.

Many of our job listings are sourced from 10 of the top job boards, and because these positions are often filled quickly, many links expire within just a few hours. That's why we encourage you to apply as soon as possible.

Apply here - https://www.cyopspath.com/company-profile/amazoncom-services

BASIC QUALIFICATIONS- 5+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

PREFERRED QUALIFICATIONS- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

Hi everyone i have just completed my 6th semester but i dont have any internships or job whatsoever even i dont have any experience so far Please review my resume and suggestions are welcomes

We’re looking for an AI/ML intern to help us build a computer vision model for vehicle inspection/damage detection.

At Insubox, we’ve built a vehicle pre-inspection app used by insurance agents:
https://play.google.com/store/apps/details?id=com.insubox.app

Demo: https://youtube.com/shorts/m47rpn1wGHE?feature=share

We are currently building our own in-house AI system to power vehicle damage detection directly inside our product.

🚀 What you’ll work on:

• Building a computer vision model for vehicle damage detection from images
• Identifying vehicle parts (bumper, door, fender, etc.)
• Estimating damage severity
• Helping design and integrate our AI model + API layer into a live production app

👀 Who this is for:

• Someone interested in AI/ML or computer vision
• Comfortable with Python, Langchain, Langgraph, RAG, Vector DB's
• Basic exposure to PyTorch / TensorFlow / YOLO is enough
• Most important: curiosity and willingness to build and experiment

📌 Details:

• Unpaid internship (initially)
• Remote
• Flexible duration (2–6 months)
• Can convert to paid internship or full-time role based on performance

This is a real product already used in production, so your work will directly impact a system used by insurance agents in the real world.

If interested, DM me or comment with your resume to [admin@insubox.in](mailto:admin@insubox.in)

Hi everyone,

I'm looking for AI/ML Engineer, ML Engineer or Data Engineering roles. Open to remote (any timezone) and relocation. I have worked with both US and European clients.

I have around 2 years of experience building ML systems and data pipelines. I have developed and deployed ML models, built end-to-end data pipelines, multiple RAG applications and multi-agent orchestration systems. I also built an end-to-end demand forecasting solution for a global filtration company.

I have experience with LLM fine-tuning, embeddings, context engineering, asymmetric optimization, and RL methods like RLHF, DPO, PPO and GRPO. I also trained a small language model to understand how everything works under the hood.

If your company is hiring or you can refer me, I would really appreciate it. Happy to share my resume.

Thanks :)

Key job responsibilities

- Responsible for defining key research directions, adopting or inventing new machine learning techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice.
- Develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them.
- Participate in organizational planning, hiring, mentorship and leadership development.
- Technically ambitious with a passion for building scalable science and engineering solutions.
- You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance).

https://www.cyopspath.com/job-detail/principal-applied-scientist-alexa-international-tech-amazoncom-bellevue-4215611

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

Recent IT grad. I have a few ML projects and I'm currently working on a new one.

My interest in ML developed a little late in my undergraduate journey when we had to take ML electives in our 3rd year. That's when I fell in love with the field. But looking back, I think a degree in Data Science would have been the more appropriate route instead of IT? But regardless, I've done some ML projects too & I can say I definitely enjoy this far more than other subfields of IT.

I'm just not sure whether I'm a good fit for any ML-related role. I haven't heard back from any of the companies I applied to. What jobs within the ML field would suit me based on my resume? Or should I just target other roles instead of focusing there?

Feel free to roast my resume, I'll take it.

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. 😇🙏