• Design and develop cutting-edge Generative AI models.
• Fine-tune and adapt pre-trained LLMs for specific use cases.
• Evaluate and measure the performance of Generative AI models.
• Collaborate with data engineers and product teams to integrate Generative AI models into production.
• Automate model training, testing, and deployment processes using MLOps pipelines.

• Creating the overall structure and design of AI/ML systems, ensuring alignment with business goals, scalability, and performance requirements.
• Evaluating and choosing appropriate technologies and frameworks that best fit the system's requirements.
• Providing technical leadership and guidance to development teams, facilitating knowledge sharing and best practices in AI/ML development.
• Ensuring seamless integration of AI/ML components with existing systems and infrastructure, including cloud platforms like GCP/AWS.
• Conducting regular code reviews to ensure that the code meets the quality standards, follows the architectural guidelines, and adheres to best practices.

• Designing, developing, and implementing comprehensive machine learning systems, including data preprocessing, model training, evaluation, and deployment in a production environment.
• Constructing MLOps pipelines to streamline the development, experimentation, continuous integration (CI), continuous delivery (CD), validation, and monitoring of AI/ML models.
• Analyzing large datasets to identify trends, anomalies, and meaningful patterns that can inform decision-making and model development.
• Assisting junior team members by providing guidance, conducting code reviews, and sharing expertise to foster a collaborative and continuous learning environment.

• Design, develop, validate, deploy and maintain various statistical, machine learning, and deep learning models for NLP and Computer Vision systems.
• Build and deploy MLOps infrastructure on Google Cloud Platform for continuous data validation, model deployment, periodic model retraining, versioning, and performance monitoring.
• Create ETL/data pipelines to collect, clean, transform, and aggregate data into data warehouses.
• Optimize data pipelines to minimize data extraction, training time, and model prediction serving latency.
• Design and implement cloud infrastructure on Google Cloud Platform to automate processes.

• Preprocess data, including cleaning, normalization, and transformation for various machine learning tasks.
• Design, develop, and train custom machine learning models for both NLP and computer vision applications.
• Fine-tune and optimize state-of-the-art models for optimal results in specific applications and tasks.
• Analyze and interpret ML model outcomes to derive insights and assess performance.
• Deploy machine learning models into production using tools and platforms like Flask/Django and GCP.
• Regularly experiment with new techniques, update models based on performance, and drive continuous improvement and innovation in machine learning systems.

North South University
Bachelor of Science in Computer Science & Engineering
Dhaka, Bangladesh
January 2015 - April 2019