Hire Generative AI developers

I started my journey in software development with a strong educational background, earning an M.Sc. from the London School of Economics. My passion for technology led me to specialize in iOS development, where I've had the privilege of working on groundbreaking projects like Rivian's flagship mobile app for vehicle control. Recently, I've delved into new technologies like GraphQL and Combine, always aiming to stay ahead of the curve. What sets me apart is my managerial experience and relentless focus on continuous improvement, ensuring that the products I work on are not just functional but exceptional. I pride myself on my diverse skill set, which includes proficiency in multiple languages and frameworks. I'm excited to bring this unique blend of skills and experience to innovative AI/ML roles. I recently did a course in Python for data science at UBC, as part of the Key Capabilities in Data Science certificate program there, and earned a grade of 95%.

Soy el solucionador de problemas que tu equipo necesita. Impulsado por una curiosidad sin límites y una dedicación a la investigación, mi dominio en los datos, el trading algorítmico, y el machine learning me permite transformar complejidades en claridad. Veo en el análisis de datos no solo un desafío, sino una oportunidad para innovar y optimizar. Con cada dataset que analizo y cada modelo que perfecciono, busco no solo resolver problemas sino anticiparlos, aplicando un enfoque que equilibra precisión técnica con visión estratégica.

Specialized in Artificial Intelligence (Computer Vision) Interested in Natural Language Processing, LLM Fullstack Web Development Learning LLMOps and Prompt Engineering

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.

Seeking to use technology as a means of improving people's lives through AI/ML. As the transistor revolution has shaped today's society, I believe AI/ML is the way forward. To this end, I focused my master's studies on NLP, and I currently work as a Data Scientist/ML Engineer at the CESAR innovation center, which produced the following achievements: A Multigenre Part-of-Speech Tagger for Brazilian Portuguese (Porttagger): current state of the art based on the BERTtimbau language model (Portuguese BERT) Lip (image) segmentation and lipstick application for people with visual impairments My expertise extends to programming in Python and utilizing a range of essential libraries, including PyTorch, MLFlow, Hugging Face, OpenCV, and more. I thrive on tackling complex challenges in AI/ML and am excited to explore how these technologies can continue to drive positive change in our world.

I worked for 3 years as a Lead fullstack developer in a startup studio where I worked with many startup founders to help them develop their project. I have been working for about a year as CTO in a legal tech startup : Norma. I worked on multiple startup projects. Lately I’ve been passionate about generative AI and the possibilities it offers. I decided to focus on generative ai projects, langchain, ai agents, and everything in between.

New grad looking to for a role with opportunities to solve interesting and complex problems. Strong software engineering, math, and stats skills. Software engineering, machine learning engineering, and research experience. Passionate about language, computer vision, and automation. Most recently created a novel machine translation model utilizing dependency trees as interlingua.

With a robust theoretical background in understanding skill development, I have dedicated significant research towards the acquisition of high-level competencies in individuals with no prior training. My research in cognitive science on learning has been deeply influenced by both constructivist and behaviorist approaches. I have penned a thesis on this subject, further solidifying my expertise in the domain. My eclectic experience spans across teaching, training, and now, in the realm of information technology. Today, I seamlessly integrate all these skills, bridging the gap between education, training, and technology. Passionate about AI, I am currently advancing my expertise in the Python language and am eager to share my innovative ideas and technical skills. I am excited about the opportunity to contribute to a leading company in the AI sector, where I can leverage my unique blend of skills and experiences. Programming Languages: Python, SQL, JavaScript, HTML5, CSS3, PHP, Laravel

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

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.