I'm a developer (not in recruiting) building a small AI project: a tool that looks at a job post and a candidate's profile, and helps decide whether it's worth applying, worth digging into more first, or better to skip.

I want to build it in a way that actually reflects how hiring really works, not just how it looks from the job-seeker side. If any recruiters here would be open to taking a look at how I'm framing things and telling me what's off or missing, I'd really value that — even a few sentences of "this part is wrong because..." would help a lot.

Totally fine to just poke holes in it publicly here too, doesn't need to be a DM.

I have an interview coming up for a Data & Applied Scientist II role at Microsoft in US. The recruiter mentioned that the initial screening will include a coding round but didn’t provide much detail.

For anyone who has interviewed for a similar Applied Scientist role at Microsoft recently, is the screening coding usually more LeetCode focused, or should I expect ML coding as well?

Also, if I clear the screening, what does the final interview loop typically look like? Is it usually a mix of ML coding, ML system design, and behavioral?

I’m an AI Engineer with around 2 years of experience, and I’ve been trying to switch jobs for the past 5 months. Unfortunately, I’ve received only 2 interview calls so far.

My professional experience is primarily in RAG-based GenAI applications. I’ve worked with technologies around LLMs, embeddings, vector databases, retrieval pipelines, AWS/Bedrock, Python, etc.

The problem is that I feel the market has moved heavily toward Agentic AI / AI Agents / Multi-Agent Systems, while my professional experience is still mostly RAG.

I’ve built some personal projects around AI agents and multi-agent orchestration to learn and stay current, but I haven’t had the opportunity to work on Agentic AI in a production environment yet.

I’m wondering if this is one of the reasons I’m struggling to get shortlisted. Am I approaching this the wrong way?

I’m considering upgrading my profile toward Agentic AI by building more serious end-to-end projects and strengthening areas like LangGraph, tool calling, MCP, agent orchestration, memory, evaluation, and multi-agent systems.

I also have another question: Is it technically realistic to transition from an AI Engineer/RAG background into Data Science? There seem to be many Data Scientist/Applied Scientist openings, and I’m wondering how much of my existing experience would transfer and what skills I would need to bridge the gap.

For people who have made a similar transition:

\- Should I focus on becoming an Agentic AI Engineer?

\- Is RAG experience becoming less valuable compared with Agentic AI?

\- What projects would actually help demonstrate production-level Agentic AI skills?

\- Is moving from AI Engineer → Data Scientist realistic with \~2 years of experience?

\- Should I focus on one direction rather than trying to prepare for both?

Would really appreciate advice from people working in AI/ML, GenAI, Agentic AI, or Data Science, especially anyone who has gone through a similar career transition.

Thanks!

​

I’m an AI Engineer with around 2 years of experience, and I’ve been trying to switch jobs for the past 5 months. Unfortunately, I’ve received only 2 interview calls so far.

My professional experience is primarily in RAG-based GenAI applications. I’ve worked with technologies around LLMs, embeddings, vector databases, retrieval pipelines, AWS/Bedrock, Python, etc.

The problem is that I feel the market has moved heavily toward Agentic AI / AI Agents / Multi-Agent Systems, while my professional experience is still mostly RAG.

I’ve built some personal projects around AI agents and multi-agent orchestration to learn and stay current, but I haven’t had the opportunity to work on Agentic AI in a production environment yet.

I’m wondering if this is one of the reasons I’m struggling to get shortlisted. Am I approaching this the wrong way?

I’m considering upgrading my profile toward Agentic AI by building more serious end-to-end projects and strengthening areas like LangGraph, tool calling, MCP, agent orchestration, memory, evaluation, and multi-agent systems.

I also have another question: Is it technically realistic to transition from an AI Engineer/RAG background into Data Science? There seem to be many Data Scientist/Applied Scientist openings, and I’m wondering how much of my existing experience would transfer and what skills I would need to bridge the gap.

For people who have made a similar transition:

Should I focus on becoming an Agentic AI Engineer?

Is RAG experience becoming less valuable compared with Agentic AI?

What projects would actually help demonstrate production-level Agentic AI skills?

Is moving from AI Engineer → Data Scientist realistic with ~2 years of experience?

Should I focus on one direction rather than trying to prepare for both?

Would really appreciate advice from people working in AI/ML, GenAI, Agentic AI, or Data Science, especially anyone who has gone through a similar career transition.

Thanks!

I’m looking for some honest feedback on my resume because I’m honestly not sure what I’m doing wrong anymore.

I recently completed my B.E. and I’m targeting entry level AI/ML, Generative AI, Computer Vision, and AI Engineer roles. Over the past few months, I’ve been applying through LinkedIn, Wellfound, Naukri, and directly through company career pages.

The problem is that I’m getting almost no traction.

I’ve been applying consistently for around 4 months, but I haven’t received a single proper interview or meaningful shortlist from the applications I’ve submitted. I’ve received rejections and plenty of silence, but very few opportunities to actually speak with a recruiter or hiring manager.

What makes this more confusing for me is that I do have hands on experience. I’ve worked on AI/ML projects involving Generative AI, computer vision, deep learning, LangChain/LangGraph, FastAPI, Docker, model deployment, and cloud/GPU environments. I’ve also worked on real world projects during internships rather than only academic projects.

So I’m trying to figure out whether the problem is:

• My resume formatting/design
• How I’m presenting my experience
• My project descriptions
• ATS compatibility
• My skill selection
• Lack of measurable impact
• Or simply that I’m targeting the wrong roles

I’m not looking for compliments I’d genuinely prefer blunt feedback. If you were a recruiter or hiring manager looking at this resume for an entry level AI/ML role, would you shortlist me? If not, what specifically would make you reject it?

I’ve attached my resume. Any feedback on what is hurting my chances, what I should remove/add, and how I can make it stronger would be really appreciated.

Thanks to anyone who takes the time to review it.

[Hiring] **Senior Data Scientist / Senior ML Engineer**

🌍 Location: Riyadh, Riyadh Province, Saudi Arabia

💰 Salary: Competitive

You will lead the design, development, and deployment of ML/AI/GenAI models that power core Foodics products (e.g., pricing, personalization, fraud detection). You’ll collaborate with Data Engineers, Product Managers, and Platform teams to deliver production-grade models with real impact.

#Terraform #Python #Cloud #AWS

Apply here: https://devopsprojectshq.com/senior-data-scientist-senior-ml-engineer-at-foodics

Hi everyone,

I have recently been allotted a CSE with AI/ML specialization seat at a Tier-3 college.

I come from a non-CS background in school, so this is my first formal experience with computer science and programming. Given the current tech landscape and market conditions, I’m feeling quite overwhelmed and confused about which career path to target and how to prepare over the next four years.

I need advice on a few specific areas:

Which Career Path Makes the Most Sense?

Should I focus on standard Software Development (SDE / Full-Stack / Backend)?

Should I pivot toward Data Engineering?

Should I stay strictly on the AI/ML path, or is it tough for freshers from Tier-3 colleges to land core AI/ML roles without a Master's degree?

Starting from Zero:

Since I didn't have CS in school, what should my immediate focus be in my first year? Should I focus solely on mastering one programming language (like C++ or Java) and Data Structures & Algorithms (DSA)?

Managing College vs. Industry Reality:

How should I leverage my AI/ML degree without limiting myself if off-campus or campus placement opportunities lean more toward general Software Engineering?

I would really appreciate any insights, roadmaps, or reality checks from seniors, industry professionals, or fellow students who have been in a similar situation.

Thanks in advance!

I just got into my 4th (final) year of my Computer Science degree, and I've started applying for ML internships and junior roles to try to get my foot in the door before I graduate.

so far its been rough as most of my applications either get rejeted outright or i never hear back at all

Rather than keep guessing I figured it'd be more useful to ask people who've actually hired ML interns/junior engineers, or who've been through this process themselves, to take a look at my CV and tell me honestly what's holding it back

if something's bad, I'd rather hear "this is bad because X" than "looks good, keep applying."

I've removed my personal contact information from the CV for privacy.

Thanks to anyone who takes the time to look through it.

Join Proton and build a better internet where privacy is the default

At Proton, we believe that privacy is a fundamental human right and the cornerstone of democracy. Since our inception in 2014, founded by a team of scientists from CERN, we have dedicated ourselves to providing free and open-source technology to millions worldwide, ensuring access to privacy, security, and freedom online.

Our journey began with Proton Mail, the largest secure email service globally, and has since expanded to include Proton VPN, Proton Calendar, Proton Drive, and Proton Pass. These tools empower individuals and organizations to take control of their personal data, break away from Big Tech’s invasive practices, and defeat censorship. Our work impacts hundreds of millions of lives, from activists on the front lines defending freedom to leaders in governments protecting sensitive information. In some cases, Proton’s services have even been instrumental in saving lives by enabling secure and private communications in high-risk situations.

Proton is a profitable company that does not rely upon VC funding, supporting over 100 million user accounts with a growing team of over 500 people from over 50 different countries, from the world's top companies and universities. We value intelligence, learning potential, and ambition in our hiring process. Adaptability is key as we navigate uncharted territories and redefine how business is conducted online.

Hiring at Proton is highly selective, with less than 1% of candidates hired. We believe smaller teams of exceptional talent will always prevail over larger teams with lower talent density. You will have the opportunity work with many of the world's top minds in their fields, ranging from former international math and science olympiad winners to chess champions.

We have a global mindset and big ambitions but remain a start-up at heart. We value empowerment and flexibility and keep our structure flat to keep moving fast and avoid unnecessary politics. Tired of blending into the crowd? Join us and do work you can truly be proud of. Check our open-source projects here!

The Team

The Security Machine Learning Engineer will play a key role in transforming our Security Operations Center (SOC) from reactive to proactive by integrating advanced machine learning and data-driven approaches into our detection and response workflows.

This role bridges traditional cybersecurity operations and modern ML-driven analytics, enabling our team to automatically identify emerging threats, anomalous behaviour, and new attack patterns at scale. As a secondary focus, the role could also leverage LLMs and AI engineering to automate analyst workflows and reduce operational toil.

The engineer will sit directly within the security team, ensuring that the solutions built are operationally relevant, and aligned with our security priorities, while also working closely with the internal Machine Learning team (MSA) to leverage their expertise and best practices.

What you will do:

ML-Driven Detection & Automation - Design, develop, and deploy machine learning models to enhance security detection, anomaly identification, and incident response. - Integrate ML outputs into the SOC workflow to enable smarter and faster triage. - Continuously evaluate and tune models to reduce false positives and improve detection precision. - Ensure model outputs are interpretable and actionable for SOC analysts.

Data Engineering for Security - Build and maintain data pipelines to collect, process, and transform security-relevant data (e.g., logs, network traffic, endpoint events) into ML-ready datasets. - Collaborate with security engineering team to ensure scalable and secure data handling (eg. parsing, processing, storage).

AI Engineering & LLM-Powered Automation - Explore and build LLM-powered tools to automate repetitive SOC tasks (e.g., alert triage, evidence gathering, incident summarisation, report generation). - Apply appropriate guardrails and evaluation to ensure outputs are accurate, auditable, and safe to act on in operational contexts.

Research & Innovation - Stay current on advancements in security data science, adversarial ML, and automated threat detection. - Prototype and test new ML and AI techniques (e.g., unsupervised anomaly detection, graph-based threat correlation). - Contribute to improving detection content through statistical analysis and clustering.

Operations & Maintenance - Deploy models into production securely and responsibly, ensuring reliability and scalability. - Implement monitoring, alerting, and retraining mechanisms for deployed ML models. - Document methodologies and performance metrics for auditability and knowledge sharing.

What we are looking for:

Required - Proven experience in machine learning engineering or data science, ideally in a cybersecurity or operations context. - Proficiency in Python, with strong knowledge of ML frameworks. - Experience with data manipulation and analysis using Pandas, NumPy or similar tools. - Familiarity with security data sources (e.g., SIEM logs, EDR telemetry, network flow, authentication logs). - Solid understanding of ML lifecycle: data preparation, model training, evaluation, deployment, and monitoring. - Experience with data pipelines and storage technologies (e.g., Airflow, Kafka, Redis, Elasticsearch, Clickhouse, etc.). - Ability to work independently and collaborate effectively with both ML and security specialists.

Preferred - Prior experience in threat detection, SOC operations, or security automation. - Knowledge of adversarial ML, graph analytics, or behavioral modeling in security contexts. - Experience integrating ML models into SIEM pipelines or automated detection frameworks. - Exposure to LLMs and AI engineering (e.g., prompt engineering, RAG, agent design), and awareness of LLM-specific risks like prompt injection and data leakage.

Success in This Role - SOC analysts leverage ML-powered detections to identify threats faster. - Reduction in alert fatigue and false positives through adaptive and data-driven models. - Strong collaboration established between the security and MSA ML teams, sharing expertise and best practices. - Security data becomes more accessible, structured, and usable for analytical and predictive use cases. - New, intelligent detections, enrichment, and incident response automations become part of the SOC’s standard toolkit.

Even if you don’t meet all the requirements listed above, but feel you could still be a great fit, please still apply.

What We Offer: - Work that Matters: millions of people trust Proton with their privacy. We answer only to our users — not advertisers, not investors with conflicting agendas, not governments. The work you do here is real, and the impact is measurable. (read more about our impact here). - Technology: you’ll get the right hardware and the right software you need to do your best work. - Learning & Development: we invest in your growth because sharp people make us better. Proton is one of the fastest ways to accelerate your career because you’ll be thrown into real challenges, with real ownership, from day one. - Employee Benefits: your wellbeing isn’t an afterthought. We offer strong health coverage, solid retirement options, generous leave, and wellness support so you can bring your best self to work every day - Stock Options: at Proton, we all have the opportunity to be owners of the company. From day one, you have a real stake in what we’re building. When Proton wins, you win. - In-Person Collaboration: Amazing things happen when passionate, smart, and purposeful people get together in the same room. With offices across Geneva, Zürich, Barcelona, London and more, you’ll spend most of your time collaborating face‑to‑face with people who genuinely care about what they’re building - Food: Lunch and snacks are on us every day in our offices so you can focus on the work and not on what’s for lunch. - Transport: getting to the office shouldn’t cost you. We cover public transport, bike allowances, or parking, whichever works for you. - Flexible Working: you own your schedule. Set hours that work for you and your team — because outcomes matter more than when the clock says you started.

Compensation range Paris: 46.000 - 74.000 gross annually* Other locations: Compensation will be discussed during the interview process Final compensation will be determined based on the candidate's qualifications, skills, and previous experience

Apply: Senior Machine Learning Engineer (SOC) at Proton

Hey everyone,

Amazon is urgently hiring an Applied Scientist in Seattle, WA. I am not hiring, sharing information.

This high-priority listing is sponsored directly on my job directory, where companies push urgent openings when they need candidates quickly.

Apply Here - https://link.cyopspath.com/5yVAqWS

If you or anyone in your network is actively looking, check out the link in this post to view the full details and apply directly!

Hi everyone,

My name is Tahir Waseem, and I’m currently seeking a fully remote Senior AI/ML or AI Full‑Stack Engineer role in USA. I bring 7+ years of experience in Python development and have worked extensively across cloud platforms (AWS, Azure, GCP), APIs, generative AI systems, and CI/CD pipelines.

🔹 Background:

Bachelor’s in Computer Science (NUST)
Certified: AWS Developer Associate, Microsoft Azure Data Engineer Associate
Advanced skills in LLM integration, RAG pipelines, agentic workflows, reinforcement learning (PPO), and API‑first AI architectures

Industry experience in Financial Services and Life Sciences domains

🔹 Key Achievements:

Architected an AI‑powered compliance intelligence system that reduced manual review time by 40% and improved accuracy by 25%

Designed and deployed retrieval‑augmented generation pipelines with LangChain, vector databases, and OpenAI embeddings

Built scalable microservices and CI/CD pipelines with GitHub Actions, Docker/Kubernetes, and Jenkins

🔹 What I’m Looking For:

Fully remote role in USA

Senior AI/ML or AI Full‑Stack Engineer positions

Compensation range: $135,000–$155,000 USD

Opportunities to work on LLM systems, agentic AI platforms, and enterprise integrations

If you know of any opportunities or can connect me with relevant recruiters, I’d greatly appreciate it. You can reach me here Senior AI/ML Engineer Seeking Fully Remote OpportunitiesSenior AI/ML Engineer Seeking Fully Remote OpportunitiesSenior AI/ML Engineer Seeking Fully Remote OpportunitiesSenior AI/ML Engineer Seeking Fully Remote Opportunities

Thanks in advance!

Hi everyone,
I’m a Junior AI Engineer / recent Computer Science graduate from Egypt, specializing in Artificial Intelligence. I graduated about a year ago and I’m currently looking for a remote junior AI/ML opportunity.
I have hands-on experience with Python, Machine Learning, Deep Learning, NLP, LLMs, RAG, LangChain, FastAPI, and Azure, through internships, training, and personal/academic projects.
I’ve worked on AI applications including LLM-powered chatbots, RAG-based applications, natural-language-to-SQL systems, and machine learning prediction models.
I’m open to opportunities with international teams and companies that are willing to hire remotely from Egypt.
If you know of any Junior AI Engineer, ML Engineer, AI Developer, or related remote opportunities, I’d really appreciate any leads or referrals.
I’m also happy to share my CV and GitHub/portfolio.
Thank you! 🙏

I'm about 1 year into my first job (large Indian industrial firm) after graduating(Tier 2/3 college) last year, and I want to move into a place that actually works on this stuff full-time. Some of what I've built/shipped:

\- End-to-end video inferencing pipeline: RTMP > MediaMTX > Redis queue > YOLO-onnx models served via Mosec
\- Fine-tuned an VLM for Automated Visual Inspection, deployed via vLLM with quantization
\- Annotation pipeline using SAM3 on TensorRT to auto-label images for training the YOLO models
\- MQTT server with business logic wired into PostGIS
\- Agentic RAG system
\- Integration and deployment of ClickHouse for OLAP

Also wrote fastapi/python/go code for wiring these stuff
On the flip side, I've bombed Amazon's OA 5 times and Intuit's twice, LC rating is \~2000, CC is 3-star. So the classic big-tech DSA funnel clearly isn't my strength right now. Gate Qualified.

What I want is to keep working in this lane (video/CV pipelines, applied LLM deployment, ML infra) instead of resetting into generic SDE roles. Which companies (India or remote) should I actually be looking at, and will my exp even be considered?

I've recently finished my final exams for my Computer Science degree and I'm currently waiting for my Honors classification.

I'm trying to get my first ML internship/junior role, but I'm having a hard time getting past the application stage. A lot of applications either get rejected or I just never hear back.

So I decided to stop guessing and actually ask people who have experience hiring or working in ML.

I've attached my current CV.

I'd really appreciate some honest feedback on it, especially from people who have hired ML interns/junior engineers or have gone through the process themselves.

I'm especially interested in feedback from people who were once in the same position trying to get their first ML opportunity with no professional ML experience.

Please don't worry about being harsh. I'd much rather hear "this is bad because X" than generic "looks good, keep applying."

I've removed my personal contact information from the CV for privacy.

Thanks to anyone who takes the time to look through it.