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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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Senior Data Scientist: Machine Learning / AI

I am a data scientist with 7 years of experience as a freelancer in diverse industries. My mission is enabling my clients and the teams I work with to implement data-driven innovation, making the connection between use cases, algorithms, and the appropriate tech stack. I am mainly focused on machine learning from prototype to deployment, but my work also regularly involves optimization and data mining. Data-driven Innovation – end-to-end To make data-driven innovation succeed, I work with my clients in all phases of a data science project – including business case discovery, structuring of requirements, selecting appropriate methods, algorithms and tech stacks, and finally deployment and evaluation in production. Full Stack Data Science Full stack refers to the software engineer who is familiar with both the user-facing side and the backend infrastructure of software. Similarly, the full stack data scientist understands the analytics methods as well as user interaction and infrastructure needed to put it all into practice – a fitting description of my typical role in data science projects. I am especially interested in establishing software engineering best practices in the field of data science. Pragmatic AI Artificial Intelligence is currently both overhyped and underrated. We have seen some remarkable progress in AI capabilities, yet many companies struggle to put intelligent systems to work on everyday use cases. Work with me to go beyond the hype and connect business cases to technology – because many valuable, pragmatic cases for self-learning systems are within reach for your enterprise.

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Versatile, innovative, and detail-oriented developer with a passion for problem-solving and a strong foundation in various programming languages. Skilled in developing robust and scalable applications, leveraging cutting-edge technologies, and adapting quickly to new challenges.

What sets me apart as a developer is my unwavering passion for Machine Learning systems. I am genuinely enthusiastic about exploring the possibilities that machine learning brings to the table, and I am always eager to push the boundaries of what can be achieved with this transformative technology. My unique perspective on Machine Learning stems from a blend of academic knowledge, practical experience, and a creative mindset. With a strong educational background in machine learning algorithms, statistical analysis, and programming languages, I possess a solid foundation that enables me to understand the theoretical aspects of the field. Moreover, I actively stay up-to-date with the latest advancements in the Machine Learning domain, continuously expanding my knowledge base to stay at the forefront of this rapidly evolving field. What truly distinguishes me is my ability to translate complex machine learning concepts into practical solutions that deliver tangible results. I have hands-on experience in developing and deploying machine learning models across various industries, ranging from healthcare and finance to e-commerce and recommendation systems. I thrive in taking on challenges and devising innovative approaches to solve real-world problems using machine learning techniques. In addition to my technical skills, I excel at collaborating with cross-functional teams and effectively communicating complex technical ideas to non-technical stakeholders. This enables me to bridge the gap between cutting-edge technology and business objectives, ensuring that Machine Learning solutions align with strategic goals and deliver tangible value to the organization. As a developer, my dedication to continuous learning and staying at the forefront of Machine Learning advancements allows me to bring fresh perspectives and innovative ideas to the table. I embrace new technologies, frameworks, and methodologies, always striving to optimize processes, enhance efficiency, and drive meaningful outcomes. In summary, my passion for Machine Learning systems, combined with my strong technical foundation, creative problem-solving skills, and ability to bridge the gap between technical and business domains, make me a unique and valuable asset as a developer. I am eager to contribute my expertise and drive impactful outcomes for potential employers in the exciting field of Machine Learning.

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