Dibya Chakravorty
Senior Python Developer @Tech-5
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WORK HISTORY
Senior Python Developer @Tech-5
Munich, DE
Lead AI developer, automating invoice text extraction using Azure / OpenAI / Powerautomate in the pharma / bio sector (client: Fundess)- Lead AI developer, supporting development of a news agent using Azure / OpenAI in the finance / insurance sector (client: AICX)- Cloud architect, responsible for the production deployment of an algorithmic trading system (client: Techno Trading) on AWS, with focus on fault tolerance, high security and cost optimization- Quality assurance lead of the OpenDesk portal (client: Univention), with focus on CI/CD and test automation- Team Lead, test automation framework development and devops in the automative sector (client: Mission EC).
EDUCATION
Indian Institute of Science Education & Research (IISER), Kolkata
Master of Science (MS), Theoretical and Mathematical Physics
Patha Bhavan
High School, Science and Technology
SKILLS
ABOUT DIBYA CHAKRAVORTY
I am a Python generalist with broad experience in machine learning, scientific computing, backend development, software quality assurance, and cloud architecture. My current work focuses on enabling organizations to automate well-structured, repeatable processes with clearly defined quality metrics using AI.Alongside my professional role, I am engaged in long-term research, educational, and community-related activities focused on foundational and technical challenges in artificial intelligence. My research interests center on the capability gap between state-of-the-art AI systems and AGI, including agentic reinforcement learning, learning in non-verifiable domains, continual learning, open-endedness, multi-agent learning, and world models. My most recent work focuses on ARC-AGI-2.I am involved in building independent technical communities in Munich, organizing the Munich AGI Meetup since 2023 and serving as a co-organizer of the Munich Python Meetup since 2018.I also contribute to industry-relevant AI education, having taught courses on practical deep reinforcement learning and quality assurance in machine learning applications.
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