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Mr. Dulanga Sashika

Senior Consultant at Visa

BSc Eng. (Hons) (Moratuwa)

As a software engineer with over 𝟵 𝘆𝗲𝗮𝗿𝘀 of experience, I specialize in designing and developing enterprise applications in the 𝗙𝗶𝗻-𝗧𝗲𝗰𝗵, 𝗔𝘃𝗶𝗮𝘁𝗶𝗼𝗻, and 𝗣𝗿𝗼𝗰𝘂𝗿𝗲𝗺𝗲𝗻𝘁 sectors. Currently, I lead teams by providing technical leadership and architecting solutions. In my most recent project, I spearheaded the migration of legacy batch jobs to open system technologies using Spring Batch, enhancing system performance and scalability.
𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲:

Proficient in Java, Spring Boot, Spring Batch, SQL, and Angular.
Experienced in microservices architecture, Kafka, Hazelcast, and cloud technologies.

𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗮𝗻𝗱 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻:

Guided teams through time-sensitive projects and mentored colleagues.
Collaborated effectively with cross-functional teams to deliver reliable and scalable systems.

𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗖𝗼𝗺𝗺𝗶𝘁𝗺𝗲𝗻𝘁:

Dedicated to providing maintainable solutions for complex challenges.
Recognized by management for contributions to project success.
Adept at understanding and analyzing user requirements.
Capable of working independently with minimal supervision.

I am always open to connecting with industry professionals to share insights and explore potential collaborations. Feel free to reach out if you'd like to discuss technology, leadership, or innovative solutions in software engineering.

Keywords: Natural Language Processing | Machine Learning / Deep Learning |

Publications

Theses: BSc

Nadeeshaan Gunasinghe, Dulanga Sashika, Anushka De Silva, and Thilina Premasiri, "Warana: Recruitment Helper for Enterprises", University of Moratuwa, 2015

Conference Papers

U. L. D. N. Gunasinghe, W. A. M. De Silva, N. H. N. D. de Silva, A. S. Perera, W. A. D. Sashika, and W. D. T. P. Premasiri, "Sentence Similarity Measuring by Vector Space Model", in Advances in ICT for Emerging Regions (ICTer), 2014 International Conference on, December. 2014, pp. 185--189. doi: 10.1109/ICTER.2014.7083899

Projects

Low-Resource Adaptation for NLP

PI: Nisansa de Silva

We propose to advance natural language processing for low-resource settings by leveraging data augmentation, transfer learning, and model adaptation techniques to overcome the scarcity of annotated data and linguistic resources.