• I have been working in the field of AI for the past 1.5 years, gaining hands-on experience.

• I focus on strong leadership, effective team management, and fostering a collaborative work environment.

• I have developed and deployed multiple AI-powered services.

• I am actively pursuing AWS Data Engineering and Machine Learning Associate (DEA/MLA) certification.

• I am passionate about exploring AI research and keeping up with the latest developments

• I’ve Assisted and mentored others in machine learning and AI projects.

• I am Passionate about building scalable, real-world AI solutions

From Concepts to Deployed AI

• Building AI, One Project at a Time
  • Every project is an opportunity to learn and innovate. From NLP chatbots to computer vision applications, I take ideas from concept to deployment, creating practical AI solutions that solve real-world problems.
• Exploring the Future of AI
  • AI is constantly evolving, and so am I. I stay up-to-date with the latest research, tools, and techniques, experimenting with new models and architectures to understand what’s next in the world of machine learning.
• From Ideas to AI Solutions
  • I transform concepts into functioning applications. Whether it’s designing a data pipeline, training a model, or deploying a service on AWS, I focus on building AI solutions that are scalable, efficient, and impactful.
• Machine Learning in Action
  • I love putting theory into practice. By building hands-on projects and experimenting with datasets, I demonstrate how machine learning can automate tasks, provide insights, and enhance decision-making in diverse domains.
• Turning Data into Intelligence
  • Data is at the heart of AI. I specialize in extracting meaningful insights from data, applying machine learning models, and deploying intelligent systems that help businesses and users make smarter decisions.

Selected Experience

Natural Language Processing (NLP):

I built chatbots, sentiment analysis tools, and text summarization models by experimenting with transformer architectures (BERT, T5), pre-trained embeddings (FastText, Word2Vec), as well as creating my own embeddings and training custom models. I applied fine-tuning techniques to adapt these models to specific tasks, resulting in applications that enhanced user engagement and automated content workflows.

Computer Vision:

Developed image recognition and object detection systems using YOLOv8/v11, FaceInsight, OpenCV, and scikit-learn. Leveraged transfer learning and custom models to enable accurate visual analysis for real-world applications, including automated inspection and image classification.

Generative AI:

Implemented text and image generation models using GANs, diffusion models, and large language models, producing creative AI outputs that demonstrate practical and experimental innovation.

Agentic AI:

Designed autonomous AI agents that perform decision-making tasks and automated workflows. I achieved this by combining reinforcement learning, planning algorithms, and simulation environments, creating systems that solve complex tasks with minimal supervision.

Traditional Machine Learning:

Built predictive models and classification systems using scikit-learn, XGBoost, and ensemble techniques. This approach enabled data-driven solutions for practical problems, emphasizing accuracy, interpretability, and reliability.

Data Science:

I designed and implemented predictive models, classification systems, and data analysis pipelines using Numpy, pandas and scikit-learn. By performing feature engineering, hyperparameter tuning, and model evaluation, I delivered accurate, interpretable, and reliable solutions for real-world problems, enabling data-driven decision-making.

AWS:

Deployed AI solutions on AWS services such as SageMaker, Lambda, and EC2, creating scalable and production-ready applications. I focused on cloud infrastructure management, pipeline automation, and model deployment, ensuring systems are robust and efficient.