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The best Data and AI plumber in existence

As a data professional with a unique blend of actuarial and data science expertise, I bring a holistic perspective to solving complex business problems with data. My journey began in the actuarial field, where I honed my skills in statistical analysis, risk mitigation, and financial modeling. However, driven by a passion for leveraging the power of data to drive business value, I pivoted into the world of data science and engineering. What sets me apart is my ability to bridge the gap between the technical and business aspects of data. Having worked in both actuarial and data science roles, I deeply understand the importance of aligning data initiatives with strategic business objectives. I excel at translating complex technical concepts into actionable insights that resonate with stakeholders at all levels. A recent milestone I'm particularly proud of is the development of a real-time marketing AI system for a boutique agency. By leveraging cutting-edge technologies like ClickHouse, DBT, Airflow, and CatBoost, I created a system that analyzes Google and Facebook analytics to inform strategic marketing decisions. This project showcases my ability to architect and implement end-to-end data solutions that deliver tangible business impact. I'm passionate about staying at the forefront of the rapidly evolving data landscape. I continuously explore and learn about new technologies and methodologies to expand my toolkit. For example, I recently delved into the world of Retrieval-Augmented Generation (RAG) and developed a chatbot that combines large language models with a knowledge base to provide accurate and context-aware responses. What truly drives me is the opportunity to leverage data to solve real-world problems and make a positive impact. Whether it's detecting fraudulent transactions, optimizing pricing models, or enabling data-driven decision-making, I thrive on tackling challenges that require a creative and analytical approach. With my unique blend of actuarial expertise, data science acumen, and strong business acumen, I'm confident in my ability to deliver innovative data solutions that drive measurable results.

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Senior software engineer / Tech Leader / AI Enthusiast

I'm a software engineer passionate about innovation and pushing the boundaries of technology. While I have over 16 years of experience building web applications, my true passion lies in AI and generative frameworks. I'm currently focused on studying Langchain, Semantic Kernels, and leveraging LLM for code generation. I thrive in research and development roles where I can understand a business's challenges and needs, then prototype solutions using cutting-edge technologies. I naturally take on a researcher position in teams and love learning about new concepts that can drive innovation. While I may not always be the strongest explainer, I strive to become a source of knowledge for colleagues on AI and its applications. My leadership abilities are still developing, but I shine as an individual contributor who can advance a company's technological capabilities. I'm particularly interested in organizations looking to get ahead of the AI revolution. My experience with Python, C#, and Node.js allows me to build prototypes and proofs of concept, though I'm always eager to pick up new languages and frameworks. Outside of work, I continue to expand my knowledge of AI through side projects and independent research. I'm passionate about this emerging field and believe generative technologies will transform software engineering. I hope to find opportunities that allow me to work with other innovative thinkers in exploring AI's possibilities and building the future. Overall, my curiosity, technical skills, and vision for the impact of AI position me to thrive in research-focused roles at companies with ambitions for real innovation.

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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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