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AI Prompt Engineer and Python Programmer

I'm an entry-level Python programmer and AI engineer who's incredibly passionate about what I do. My brother suggested I'd excel in this field, and while I was skeptical at first, once I started tinkering with AI models, it was like the sky had opened up and my head finally cleared. I didn't have to puzzle it all out by myself anymore. Hooked immediately and wanting to do more, I completed the AI Engineering Bootcamp on Udemy, taught by the brilliant minds of Michael Taylor and James Phoenix. My Python studies were moving at a snail's pace until I discovered the magic of AI. It helped me break down complex code, interpret it, and do more with it in the last 12 days than I have been able to do in the last 12 months. But I'm not just an Engineer. I've been creating Art since I was 5, and not just macaroni pictures. With a background in digital and fine art, I bring a unique perspective to the table. My in-depth knowledge of art history and techniques helps me collaborate with AI image-generating models in a way that brings out the best in both worlds. I take those hazy ideas and add an artistic twist, so the AI creates exactly what we're envisioning. My ultimate goal has always been to become a game developer, and with AI, the resources I need to do that have been cut down to a fraction of the size. I truly believe it's one of the most powerful tools in the gaming realm, and I can't wait to dive in and craft some impactful experiences. If you're on the lookout for a Python programmer and AI Prompt engineer who's enthusiastic and loves merging technology with creativity, send me a message. If you want to check out the work I have made, both with and without AI, check out my Website and Artstation respectively, linked on my profile.

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A dynamic and forward-thinking Full-Stack/AI Solutions Engineer with over 20 years of diverse experience. Passionate about integrating cutting-edge AI technologies with full-stack development to create innovative, customer-focused solutions.

David Elbel’s unique trajectory in the field of software development, rooted in 20+ years of professional experience, is a testament to his talent, commitment, and passion for the craft. Starting as a systems administrator, he journeyed through an expansive terrain of technologies, languages, and frameworks, ultimately culminating in his current role as a Senior Full-Stack/AI Solutions Engineer. This multifaceted experience has forged a profound understanding of the tech ecosystem that is invaluable in today's rapidly evolving digital world. His developer story began with mastering Javascript in the early 2000s, followed by the adoption of Node.js in 2009. His continuous curiosity led him to freelance as a full-stack engineer where he showed not only his technical capabilities in leveraging diverse technologies like React, Vue, and Angular, but also his adeptness at Agile workflows, CI/CD pipelines, and end-to-end testing frameworks. This solid foundation has been key to his later accomplishments in the field of AI. Recently, as the CTO of StoryPrism, he achieved an impressive milestone by building an AI-driven screenplay web application platform. This project, leveraging OpenAI GPT-3 language models and image diffusion models, showcases his prowess in melding full-stack development with AI technologies to create innovative solutions. His work is testament to his creativity and forward-thinking, employing technologies like NextJS, Typescript, Python, GraphQL, and many more to achieve results. His passion and determination to push boundaries came to fruition in the creation of a fully functional ChatGPT clone. This remarkable accomplishment demonstrated his command over Python and his ability to work with emerging AI generative technologies. It also reflected his robust full-stack development background, and his ability to bring the two worlds together. David's uniqueness lies in his ability to provide a 360-degree solution to problems, with experience in both software development and AI. This unique mix of skills makes him invaluable to any team, especially in today’s AI-driven world where having a robust understanding of both realms is becoming increasingly crucial. His years of experience and the breadth of his skills, combined with his creativity and customer-focused approach, set him apart as a distinguished developer. His resume speaks to a developer who is always at the forefront of new technologies, consistently exploring, learning, and pushing boundaries.

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Langchain or llama index, those are just integrations. From fine tuning LLM for specific use case to deploying it as a system and integrating all these tools is all I do these days.

Hello everyone I started my ML journey back in late 2020 when I saw this connection between my high school math topics (like calculus, linear algebra, etc) with how neural networks work. 18-year-old me, started his journey with Andrew Ng's famous deep learning course. My early days were very much focussed on how each part of deep learning works, for example How a neuron works How convolution as an operation works How does backpropagation exactly work, etc I even wrote my first blog on medium describing the nuts and bolts of neural networks and their different types. There was a time in mid 2021, I got to learn about Graph Machine Learning. Being so fascinated, I did the same for Graph ML, like how I did for general ML and deep learning. I even ended up doing a research internship on Graph Variational Autoencoder for recommendation systems. I ended up writing my second research paper. Fast forward, I slowly started to hear this buzzword called "deployment". Initially, I thought of uploading my model file to the cloud. But it was much more than that. Slowly I started to gain knowledge about ML system design and how the backend and ML work in sync. How we can serve our ML apps by creating APIs, dockerizing them, and then deploying them on cloud services. Then I got into Major League Hacking Prep Program and I learned so much about remote work and collaborating with open source. Just after that, I got my industrial internship at a startup called voxela.ai where I learned so much about computer vision and object recognition and how it is deployed on an enterprise basis. I also learned about quantization and how to convert these models into TensorRT for fast serving. Fast forward we saw the rise in Large Language Models after the advent of chatGPT. We are seeing now so many people making tools using that in just one night and things getting viral. I always believed that it was always a bubble. And yes, I was right when I got to learn from experts that it was more about making a long-term, reliable, and efficient product with LLMs. Currently, I am working as a data science engineer intern at CorridorPlatforms. Being very interested in LLMs, I have explored and incorporated the LLM lifecycle, from fine-tuning an LLM with 4-bit quantization to compressing it further to run on a C++ backend and then serving it, doing prompt engineering for better performance, using knowledge bases to provide better context for document Q&A, to doing different LLM evaluation strategies for better and governed LLM systems. This journey has been very beautiful and remarkable for me. I believe that nothing is permanent, we are right now in an era where things are changing very fast. However it's not ONLY LLMs or ONLY Diffusion models, and we can never say classical ML is not gonna stay. Real ML is the balanced combination of everything and it's just putting the correct choice in the right place and attaching the important strings such that everything can work in sync and seamlessly. I look forward to working and helping organizations to apply my learnings and also learning more. Thanks

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