I'm a full stack AI engineer with ~6 years of experience in industry and academic research including in London.
My recent work is in Applied Multimodal Gen AI for medical imaging diagnostics, and I have built and deployed complete MVPs including multiple RAGs on AWS.
My latest work has been validated as state of the art in the field and has been selected for a chapter in a Book led by cutting edge research scientists from Oxford, Queen Mary University of London and others.
https://arxiv.org/abs/2401.01414
Hi Erik,
With over 4 years experience in the field of data engineering, I have gained my expertise in data pipelines, ETL, cloud services etc.
Please consider me for any data engineering opportunities
Senior AI and NLP Resource Person, Technology Leader, Innovator, People Manager with more than 20 years of experience in leading positions in Academics and Industry, simultaenously
Data Scientist @ BAHL | Python, ML, AI, Gen-AI, Knime, Streamlit | Converting raw data into meaningful insights, Crafting AI Solutions | Looking for international collaboration & Fully funded scholarships/RAs/TAs
I think this is a smart statement and I have my own thoughts as well. When I was in the college, which was 10+ years ago, “ data science” or “big data” were the term people started talking about. Then other terms/professions became popular, such as “artificial intelligence”, “software development” , “blockchain” , “ bitcoin”, I think in 5-10 years, when we look back, LLM would defensively bring a surge of opportunities, even more opprtunites than we can imagine now.
There are always some people who can’t catch those surges as well. For example, when I graduated, my job title was a “statistician”, because during that age, many people believe only PhDs are qualified for real “data science” jobs, if some master degree professionals claim they are “data scientists”, people will think they are bluffing. However, even after 5 years, nobody thinks like that.
I feel it will be the same for this LLM trend. In 5-10 years, we will see all the data scientists be “LLM scientists”. So far, many Gen AI teams have a preference towards PhD candidates, and I totally believe in 10 years, it will not be like the same. But that also means, almost every candidate on the job market is going to have “LLM skills” on their resume.
As a summary, if you can catch the trend, I’d say that would be helpful for your career life. But if you can’t quickly move into LLM/MLE/ML Ops area, I would say, don’t push too much on yourself, live well, live happily, work hard, always learn, and enjoy your life. And, always be positive about technology development, don’t be skeptical and believe it is just a gimmick.
ML/AI Engineer | Community Builder | Founder @Break Into Data | ADHD + C-PTSD advocate
If you want to double your salary in 1 year, follow this guide:
- If you are a Data Analyst:
Move to a Product Data Science or Data Engineering role for ML pipelines.
- If you are a Software Engineer:
Move to ML Engineering at a series B startup.
- If you are a PhD student:
Move to AI/ML research engineering roles at OpenAI, Boston Dynamics, Anthropic, Google DeepMind, Cohere, Hugging Face, etc.
Whatever you do, make sure to join the chaotic ride of LLM adoption in 2024.
Don't miss this opportunity.
Are you a pixel wizard who loves teaching machines to see the world?
If you’re fired up by deep learning, CNNs and making mind-blowing things happen with computer vision, we want YOU! 🚀
We’re hiring a Data Scientists (Computer Vision) to dive into projects where your work will have real-world impact—from crafting models that detect objects in the blink of an eye to making sense of the unseeable. This is your chance to work with bright minds, build cool things, and help shape the future!
If You’re a Fit, You’ll... 👓 Be a whiz with computer vision tools and techniques like OpenCV, PyTorch, and TensorFlow
📸 Geek out over CNNs, GANs, object detection, and image segmentation
🤓 Have a passion for machine learning and the curiosity to take it to the next level
Ready to turn pixels into purpose?
#Hiring#DataScientist#ComputerVision#DeepLearning#AIJobs
🚀 AI & Data Talent Available! 🚀
I’m excited to share my friend’s profile—he’s skilled in Generative AI, Python, Data Science, LLM Models, and Databricks, and is looking for new opportunities!
Core Skills:
- 🧠 Generative AI & LLM
- 💻 Python
- 📊 Data Science & Machine Learning
- 🔥 Databricks
If you're looking for someone with AI and data skills to drive your next project, feel free to connect or comment below!
#Hiring#GenerativeAI#Python#LLM#DataScience#Databricks#AI#MachineLearning#JobSearch
#hiring#data#MLZepto is hiring for Data Scientist/ ML Engineers!!
What will you do?
1. Analyze large datasets to extract insights and trends.
2. Build and deploy machine learning models into production.
3. Optimize algorithms for better performance and scalability.
4. Collaborate with engineers to integrate ML models into systems.
5. Continuously monitor and maintain models for accuracy.
Interested? Follow our page, comment "Interested" and DM us "Zepto_DataScientist" for the application. Make sure to follow all steps to get the application details without fail.
#DataScientist#MLEngineer#MachineLearning#ArtificialIntelligence#DataScience#DeepLearning#AIResearch#BigData#PredictiveAnalytics#DataEngineering
Data Leader | AI+Product | Turning Data into Revenue
Hi all, I’m hiring Data Scientists and Machine Learning Engineers!
My team (of 10 data scientists and MLEs) delivers AI internally to thousands of sales, customer success, and support reps.
Unlike many companies, our investment in AI isn’t lip service or a buzz word. It’s shaping our road map at all levels of the company.
Besides classic machine learning, we're diving into RAG and LLM fine-tuning.
Depending on experience and skill-level, we’ll flex on seniority.
Come join us!
✅ Unlimited PTO
✅ Remote friendly
✅ 23% YoY revenue growth
👇 Job links in comment
Dear recruiters,
If your AI Engineer job description looks like this 👇, it’s safe to say you’re not hiring one engineer—you’re assembling a Data & AI Justice League! 🦸♂️🦸♀️
I mean, who wouldn’t want a single person who can juggle Python, Kubernetes, Spark, and maybe brew coffee while spinning up an EC2 instance? ☕️🤖
But seriously, let’s cut them some slack—AI engineers are amazing, but they’re not supercomputers (yet).
👉 Follow me for more tech humor and realistic takes on the AI world.
♻️ Share if you’ve ever seen a job post that made you laugh and cry at the same time!
#AI#DataScience#MachineLearning#Python#TechHumor#CloudComputing#RecruitmentFails#GenAI#EngineeringFun
Okay okay okay, network are you ready ? 😃🥁
I'm about to share with you a very cool new opportunity 👏🏽
Are you passionate about cutting-edge technologies and pushing the boundaries of AI? 🤖
We’re on the lookout for a great talented and driven Machine Learning Engineer to join our great team! 🌟
What you’ll bring:
✅ 2+ years of experience with RecSys, NLP, or CV ML technologies.
✅ 4+ years of expertise in data structures, algorithms, and software engineering best practices.
✅ Hands-on experience with Generative AI (think text, images, and videos!).
✅ Skilled in Python, C/C++, or Java.
✅ Proven track record with distributed systems at scale, AI frameworks like LangChain, AutoGPT, and more!
✅ Passionate about LLMs and eager to dive into Prompt Engineering, Fine-Tuning, and beyond.
Bonus points if you’ve led projects, mentored teams, or have a PhD 🎓.
And bonus point number two, you get to work with mega talented people! 🧡
💡 If you’re ready to innovate and shape the future of AI, this is your moment
👉 Link to application is in the comment section and let’s create the next big thing!
#AI#MachineLearning#GenerativeAI#TechCareers#Hiring#AIInnovation
When your friend is super extroverted
vs
Me , hiring Data and ML professionals
for the last x5 years , x3 years of that on a remote island and
embracing introvert life.
Together though can help you create a competitive advantage in hiring
Data Science, Data Engineering and Machine Learning talent.
DM if you would like to find out how.
#datascience#machinelearning#dataengineering#hiring
Are your ML job descriptions hitting the mark, or are they just playing buzzword bingo?
Hiring machine learning engineers is tough—but are we making it harder than it needs to be?
Too often, job postings are packed with every trending AI term imaginable: "Deep Learning," "Transformer Models," "LLMs," "Data Engineering," and the list goes on. But are we really communicating what the role is about, or are we just trying to cast the widest net possible?
Here’s the challenge:
Job descriptions should do more than just list skills—they should tell a story.
- What unique problems will the candidate solve?
- What impact will their work have?
- How will their growth be supported?
Great ML engineers are passionate about tackling real-world challenges—not just ticking boxes on a tech stack. Let’s write job descriptions that reflect the mission behind the role and the team they’ll be joining.
To the ML community: What do you look for in a job description?
#MachineLearningOmnis Partners#AIRecruitment#TechCareers
Machine Learning Engineer | Lecturer in Computer Science
8moI'm a full stack AI engineer with ~6 years of experience in industry and academic research including in London. My recent work is in Applied Multimodal Gen AI for medical imaging diagnostics, and I have built and deployed complete MVPs including multiple RAGs on AWS. My latest work has been validated as state of the art in the field and has been selected for a chapter in a Book led by cutting edge research scientists from Oxford, Queen Mary University of London and others. https://arxiv.org/abs/2401.01414