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I am Divya Ann Kurien, an MSc Advanced Artificial Intelligence student at University College Dublin, with a strong foundation in Computer Science, Machine Learning, and statistical experimentation. I am passionate about developing innovative AI solutions and applying data-driven methods to real-world problems. I have hands-on experience building scalable backend pipelines and internal platforms using Python and C#, with practical exposure to model training, evaluation, and analysis. I enjoy turning ideas into production-ready systems and collaborating with cross-functional teams to deliver value.…I am Divya Ann Kurien, an MSc Advanced Artificial Intelligence student at University College Dublin, with a strong foundation in Computer Science, Machine Learning, and statistical experimentation. I am passionate about developing innovative AI solutions and applying data-driven methods to real-world problems. I have hands-on experience building scalable backend pipelines and internal platforms using Python and C#, with practical exposure to model training, evaluation, and analysis. I enjoy turning ideas into production-ready systems and collaborating with cross-functional teams to deliver value.

Divya Ann Kurien

Back-End Developer, Web Developer, Full Stack Developer, +2

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I am Divya Ann Kurien, an MSc Advanced Artificial Intelligence student at University College Dublin, with a strong foundation in Computer Science, Machine Learning, and statistical experimentation. I am passionate about developing innovative AI solutions and applying data-driven methods to real-world problems. I have hands-on experience building scalable backend pipelines and internal platforms using Python and C#, with practical exposure to model training, evaluation, and analysis. I enjoy turning ideas into production-ready systems and collaborating with cross-functional teams to deliver value.…I am Divya Ann Kurien, an MSc Advanced Artificial Intelligence student at University College Dublin, with a strong foundation in Computer Science, Machine Learning, and statistical experimentation. I am passionate about developing innovative AI solutions and applying data-driven methods to real-world problems. I have hands-on experience building scalable backend pipelines and internal platforms using Python and C#, with practical exposure to model training, evaluation, and analysis. I enjoy turning ideas into production-ready systems and collaborating with cross-functional teams to deliver value.

Available to hire

I am Divya Ann Kurien, an MSc Advanced Artificial Intelligence student at University College Dublin, with a strong foundation in Computer Science, Machine Learning, and statistical experimentation. I am passionate about developing innovative AI solutions and applying data-driven methods to real-world problems.

I have hands-on experience building scalable backend pipelines and internal platforms using Python and C#, with practical exposure to model training, evaluation, and analysis. I enjoy turning ideas into production-ready systems and collaborating with cross-functional teams to deliver value.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

Software Developer 1 at SOTI India

August 1, 2021 - July 1, 2025

Backend developer on the flagship product MobiControl, focusing on performance optimization, memory management, and modernization of legacy systems. Designed and implemented C# auto-generation tools to replace the AutoMapper library, reducing memory consumption by approximately 750MB and improving overall system performance. Led a team of 5 developers to migrate legacy WCF hosts to .NET 8 RESTful APIs, improving system scalability, security, and maintainability. Built automation tools to generate data access layers for SQL Server using ADO.NET, reducing development time by 60% and improving consistency.

Research Intern at IIIT Kottayam, India

June 1, 2021 - August 1, 2021

Mobility Prediction in Fog Environment: Applied Federated Learning for predicting vehicular trajectories. Built global FL model from decentralized training of fog nodes and simple averaging function using vehicular mobility trace dataset to reduce latency and maintain privacy. Achieved the lowest RMSE score of 0.0303 for LSTM based network.