
As an experienced AI Engineer, I bring a blend of technical acumen, creativity, and passion for artificial intelligence to drive innovation and deliver cutting-edge AI solutions. With a proven track record spanning several years, I have honed my skills in various programming languages, machine learning(computer vision, including object detection and segmentation.), deep learning, and natural language processing. Moreover, I have gained hands-on experience fine-tuning, hosting, and quantizing large language models, empowering businesses to leverage powerful AI-driven conversational agents and text analysis tools.
Collaborating closely with cross-functional stakeholders, I strive to bridge the gap between abstract ideas and tangible outcomes, translating complex problems into actionable AI roadmaps that unlock value and accelerate business growth. My dedication to staying current with emerging trends enables me to incorporate the most promising and innovative techniques, constantly pushing the envelope of what's possible with AI. Above all, I remain steadfast in championing responsible AI practices, advocating for equitable, secure, private, and transparent implementations that serve humanity's greater good.
Jul 2018 - Present
1 Programming Languages:
* Proficient in Python, with expertise in libraries such as NumPy, pandas, matplotlib, scikit-learn, TensorFlow, Keras and pytorch
* Familiarity with Java and JavaScript for full-stack development and front-end visualization
* Basic understanding of C and R for low-level programming and statistical analysis
2 Image Processing and Analysis:
* Specialized skills in hyper and multi-spectral remote sensing images
3 Natural Language Processing:
* Background in linguistic theories and techniques to build intelligent conversational agents and perform sentiment analysis
Quantizing large language models to reduce memory footprint and increase efficiency, facilitating real-time inference on edge devices
4 Machine Learning and Deep Learning:
* Fundamental understanding of classical machine learning algorithms, including support vector machines, random forests, gradient boosting, and k-nearest neighbors
* Mastery of deep learning techniques, such as convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks
5 Computer Vision:
* Knowledge of computer vision techniques, such as semantic segmentation, object detection, optical flow, motion estimation, stereo matching, and structure from motion
5 Cloud Computing Platforms:
* Experience in setting up and configuring instances on cloud providers like AWS, Microsoft Azure, and Google Cloud Platform, and hybride system setup
6 Virtualization and Containerization:
* Hands-on experience with Docker and Kubernetes for packaging and deploying microservices architecture