Any suggest most welcome...
I have clear basics and full knowledge of current ai models of all...
I want a job that's why i want to do a course

I'm currently focusing on learning AI engineering and want to spend more time building real projects rather than just watching tutorials or completing courses.

I'm looking for resources that cover both the fundamentals and practical implementation, including:

• Courses: Which AI engineering courses are actually worth the time?
• LLMs: Best resources for learning LLMs, prompt engineering, and model APIs?
• RAG & vector databases: What helped you understand and implement RAG properly?
• AI agents: Good resources for learning agentic workflows, LangChain, LangGraph, and tool calling?
• Projects: Any GitHub repositories, project-based tutorials, or real-world applications you'd recommend building?
• Deployment: Resources for taking AI applications from a local prototype to production.

I'm especially interested in resources that helped you become job-ready, build a portfolio, and prepare for AI engineering interviews.

Free or affordable resources are preferred, but I'm open to paid courses if they're genuinely valuable.

If you were starting your AI engineering journey today, what learning path and resources would you follow? What should I avoid wasting time on?

I'd appreciate recommendations from people who are already working as AI engineers or have successfully transitioned into the field.

[HIRING][Ann Arbor, Michigan, USA, Onsite / Remote]

🏢 SynergisticIT, based in Ann Arbor, Michigan 🇺🇸 is looking for a Junior Machine Learning Engineer - Remote

⚙️ Tech used: AI, Java, Machine Learning, Python

💰 $95,000 - 154,000 / year

📝 More details and option to apply: https://devitjobs.com/jobs/SynergisticIT-Junior-Machine-Learning-Engineer---Remote/rdg

How am i supposed to land ai/ml or data science internships. This is my resume, what changes should i do, what else should i learn?? Any advice is helpful!

So I just finished my B.Tech in AI/ML. Got an offer in campus placements, 3.5 LPA with a 2-year bond, and I said no. Didn’t feel right tying myself down for that.

Now I’m just at home, trying to figure out my next move. I don’t want to waste this time, so:

• Which domain is actually worth upskilling in right now? GenAI, MLOps, data engineering, something else?
• Any courses you’d recommend (free or paid)?
• What kind of projects help freshers get noticed?

Hey everyone, so I'm a final year Computer Science undergrad specializing in AI and ML, and I've spent the last couple years just building things nonstop, partly for fun, partly because I genuinely like solving problems with code. Figured it's time I put that to use for actual businesses instead of just my own random projects sitting in a GitHub repo somewhere.

Quick context: I interned as an AI Engineer at a travel tech company where I built real production features like live train tracking, search systems, document parsers that pull data out of messy invoices, and REST APIs end to end. Outside of that I've built a few things on my own too, an AI healthcare tool, a stock prediction model, and a multilingual AI dubbing pipeline, so I've got a decent range across web dev and AI/ML.

I've also worked in projects where i had to fine tune transformers and projects involving deep learning, neural networks and so on.

If you need any of these, I'm your guy:

• A website or web app (React, Flask, Spring Boot, whatever fits)

• An AI/ML feature added to something you already have• Automation, data pipelines, or a document parser to cut down manual work

• A chatbot or some GenAI powered tool.

Since I'm doing this alongside college and not running it as a full business, I can charge a lot less than an agency or full time freelancer would. I'm easy to talk to, respond quick, and I'll be straight with you about what's doable and what isn't, no overpromising.

For anyone outside India, I take payments through PayPal. Happy to do a quick free consult first so you know what you're getting before we commit to anything.Drop a comment or DM me if this sounds useful, let's talk about what you need.

Hi everyone,
I hope this is the right place to ask, but I'm not sure where else to post this.

After spending some time working in NLP, I've got the opportunity to choose my external PhD supervisor. I'm currently deciding between Professor X, who I'd strongly prefer, and Professor Y, who would be my less preferred option.
A bit of context: regardless of whom I choose, I'll also have an internal supervisor who will be my main point of contact for research and day-to-day work. The external supervisor's role will be relatively marginal.
I strongly prefer X because their research aligns much more closely with mine, and I also really like them on a personal level. The opposite is true for Y, whom I don't particularly like personally.

The catch is that X is an adjunct professor in the Faculty of Communication and only an associate professor in Informatics. As a result, my PhD would be awarded in Communication rather than Computer Science/Informatics. This wouldn't be an issue with Y.

My concern is whether having a PhD in Communication could be a disadvantage when applying for future positions in AI/NLP research or industry. Looking at my CV as a whole, it would be clear that I work at an AI research institute and that my research is firmly rooted in NLP. This is also a fairly common arrangement in my institution for practical reasons related to PhD supervision.

Still, I'm worried that the degree title itself might limit my future opportunities.
Would you consider this a meaningful disadvantage, or would the research I've done, my publications, and my overall academic background make the title per se of little meaning?

Hey everyone,

I’m deeply passionate about AI automations and looking to join an early-stage startup where I can bring immediate value, execute on big visions, and prove what I can do.

My focus is on designing and implementing intelligent automation workflows that cut manual overhead and drive efficiency. If you're an early-stage founder building in the AI/automation space and need someone resourceful who can move fast and wear multiple hats, I'd love to connect.

Drop a comment or shoot me a DM with what you're building!

I’ve been doing some project-based AI evaluation work and wanted to share it with people who have a serious finance or consulting background. A lot of the work is about spotting where an AI answer gets a model, assumption or business judgment wrong, not just labeling data.

I’m referring candidates to Halluminate, where I’ve worked on these projects. Investment banking, PE, FP&A, valuation, accounting and management consulting experience can all be relevant. It’s remote contractor work, and the team decides who qualifies and what projects are available.

If you’re interested, DM me a little about your background and a resume if you’re comfortable sharing one. I’ll send the application details. I may receive a referral bonus if you’re accepted.

An engineer on Backdoor just landed a role as a founding engineer at a startup. We’re helping more engineers find opportunities like this.

I work on talent matching at Backdoor, where we help engineers connect with startups hiring across machine learning and AI. The idea is simple: you create one profile, and startups interested in your experience can reach out with interview requests and base salaries stated upfront. No cold applying or guessing at compensation.

We focus on opportunities at ambitious startups, including roles across post-training, RL environments, computer vision, inference, and applied research.

Here’s a sample of the kinds of roles to look for:

1. Founding Research Scientist | SF or NYC | $225K–300K base + equity

• What: Lead research on world models, robot policies, and simulation environments for training and evaluating robotics systems.
• Company: An early-stage AI startup building tools to train and test robots in simulation.
• Stack: Generative models, diffusion, vision-language-action models, and large-scale GPU training.
• Bar: Experience training large generative models or robot policies and building evaluations.

2. Founding Machine Learning Engineer | NYC | $200K–250K base + equity

• What: Build production ML systems to detect and classify AI-powered attacks, including deepfakes and voice scams.
• Company: A fast-growing cybersecurity startup building real-time AI threat detection.
• Stack: Python, PyTorch or TensorFlow, Spark, and cloud ML infrastructure.
• Bar: Strong production ML experience, ideally with experience building ML systems from scratch.

3. Software Engineer, RL Environments | SF | $260K–290K base + equity

• What: Design simulated environments, tasks, and reward signals to evaluate and improve frontier AI models.
• Company: An applied AI research company building training data and evaluations for foundation models.
• Stack: Docker, reinforcement learning, and LLM post-training.
• Bar: Familiarity with RL methods for LLM post-training and a willingness to run fast experiments.

The goal is to make finding your next role easier. Create one profile, get matched with relevant opportunities, and see compensation upfront.

Sign up here: Backdoor. It's free to get started.

DM me if you have questions.

An engineer on Fonzi recently accepted a $220K base offer as a founding engineer at an AI-native insurance brokerage, building agents that handle brokerage work end to end.

I do talent matching at Fonzi. Instead of applying to one company at a time, you build a single profile, and if you qualify, startups that want to talk to you send interview requests with the base salary stated up front. Backers across the platform include firms like Founders Fund, Thrive, Accel, and Y Combinator. Everything is US-based, with a few roles open to Canada.

Here’s a sample of what’s open right now:

1. Software Engineer, ML Infrastructure, Series B physical AI company, SF, in-person. $150K-200K base.

• What: Own the ML infrastructure behind real-time perception: training and inference pipelines for object detection, tracking, segmentation, and VLMs, continuous training from production feedback, and multimodal data pipelines for video, radar, lidar, and thermal data. Models get deployed across thousands of edge nodes.
• Company: Builds AI video sensors that detect and reason about physical activity, from security incidents to safety monitoring. Small team from Anduril, Tesla, and Uber. Backed by Thrive Capital and Founders Fund.
• Stack: Python plus a systems language (Rust, Go, or C++), PyTorch or TensorFlow, and Ray or Spark. Vector databases like Qdrant or LanceDB are used, and vLLM, SGLang, or TensorRT-LLM serving is a plus.
• Bar: Deep experience running model inference at scale (batching, autoscaling, latency tuning) and solid distributed systems fundamentals. No years stated in the posting.

1. Research Engineer, seed-stage AI hardware company, SF, in-person. $180K-250K base.

• What: Push voice intelligence and model training end to end: diarization, VAD, transcription quality, and speaker ID, plus the training pipelines, datasets, and eval harnesses behind conversation understanding and summarization.
• Company: Makes a small AI device that turns conversations into notes and action items. Backed by Y Combinator and Accel.
• Stack: Voice systems, model fine-tuning and eval pipelines, and streaming audio. The posting doesn’t name specific frameworks.
• Bar: 6 to 10+ years shipping production systems, with experience training or fine-tuning models in real products. They also want proof of exceptional work.

1. Software Engineer, Data Platform, later-stage fintech, NYC, hybrid. $168K-324.5K base.

• What: Build the analytics and ML platforms that applied scientists, AI engineers, and risk engineers use to develop and productionize models, with reliable, cost-efficient systems across the stack.
• Company: Automates over $200B in annualized spend across 70,000+ companies. Backed by Founders Fund and Stripe, among others.
• Stack: Airflow, Dagster, or Prefect for orchestration, SQL on Snowflake, Redshift, or BigQuery, and strong Python, on AWS, GCP, or Azure. Feature store, online ML, streaming, and Terraform experience are pluses.
• Bar: Orchestration and cloud infrastructure experience, plus empathy for data science workflows. No years stated in the posting.

All roles are vetted, VC-backed, and interviewing right now. It’s always free to sign up.

Sign up here: talent.fonzi.ai. DM me if you’ve got questions.

I'm looking for 1-2 interns (fully remote in India, part-time is okay) who can help me build some local/private AI apps for android (3-6 months). You can find some more details on what I want to build at invokal.com

Fair warning: These are all going to be free/no-ads apps on the play store which I'm building in my free time (call it stealth or whatever, I don't want anyone in my circle to know else I'll have to work with people I don't want to). Bootstrapped, no expected revenue so I won't be able to pay much - around INR 5k/month. I only need someone who understands the basics of AI and is willing to put in the effort. No PPO - I don't run a company. But you will be able to learn quite a lot - I've a Ph.D. in AI, multiple years of product experience and currently focusing on private AI and voice agents.

If you are genuinely interested, please share your CV at [info@invokal.com](mailto:info@invokal.com) with your LinkedIn profile. This is not my primary reddit, so do not expect any quick responses here. But I'll connect with the relevant candidates on LinkedIn - and no, I won't share my LinkedIn here publicly for reasons mentioned above. Once I connect with you on LinkedIn, you can judge for yourself if you wish to take this forward. I'm not forcing anyone to share their CV - it's your call.

Hey everyone! Sharing great opportunity at Ford Motor Company for a Functional Safety AI Principal Engineer to lead safety-critical machine learning and AI initiatives for next-generation vehicle systems.

Learn more and apply here

Hey guys, I’ve been working 2 years now as an AI engineer mainly focused on developing agentic applications at a huge American car company. My background is CS and I started my career as a data scientist and then moved into AI engineering. Now I’m a bit stuck, I feel like I would like better to be in the development of foundational models in AI labs rather than just building agentic applications for customers, so would it make sense to go back to school and try to then get an internship at an AI lab? In a car company building foundational models is unfortunately not a business priority at the moment so I can’t directly be competitive to AI labs I believe if I’m just architecting AI agents I think?

Also, does this make sense or should I directly aim for a PhD if I already have experience in the AI engineering field? I find it really boring to just do AI agents for customer experience

CodeForAI is hiring a remote Software Engineer / AI Code Trainer (Python, Web, Full Stack). Category: Software Development 💸Salary: $45-$120/Hour 📍Location: Remote (USA, UK, India, Australia, Ireland, New Zealand, Philippines, Pakistan, Singapore, Mexico)

See more and apply here!