Chandramita Bhattacharya
AI & Data Engineer | LLM & Computer Vision Specialist
AI Engineer with a Master's in Cognitive Systems from Ulm University, specializing in enterprise RAG systems, text-to-3D generative models, and computer vision pipelines, currently building scalable AI solutions at Liebherr.
Skills
At a glance
3+
Years enterprise development
5
Research & internship projects
1.5
Master's degree (German scale)
Work Experience
Data Science Intern
Liebherr Digital Center, Ulm, Germany · July 2025 – Present
Building core components of a LangGraph-based enterprise RAG assistant for source-grounded Q&A across internal knowledge bases. Developing YOLO and SAM-based computer vision pipelines to extract gear specifications from engineering drawings, and a PyTorch-based construction-site safety monitoring solution.
Associate (Software Engineer)
PricewaterhouseCoopers (PwC), Kolkata, India · July 2019 – August 2022
Developed scalable enterprise applications using Angular and JavaScript. Engineered an internal employee portal with role-based access control and implemented analytics tracking and marketing automation workflows using Google Analytics and GTM.
Research Assistant
Ulm University, Ulm, Germany · 2022 – 2025
Supported research in multimodal material perception, 3D navigation, and spatial understanding in immersive environments.
Selected Projects
Text-to-3D Generative Model Benchmarking
Engineered a comprehensive evaluation framework for state-of-the-art text-to-3D generation systems, incorporating attribute-specific prompts, semantic alignment metrics, 3D plausibility scores, and mesh quality indicators. Implemented models on large datasets and created a community benchmark dataset.
AI-Generated Portrait Artifact Analysis
Investigated artifacts in AI-generated human portraits by developing a synthetic face dataset and robust annotation protocol. Analyzed human feedback for artifact categorization, uncovering high inter-user agreement and insights into user perception of generative AI content.
Explainable AI – Segmentation Impact Analysis
Conducted detailed analysis of image segmentation methods' impact on runtime and explanation quality of local XAI methods for CNN predictions, advancing model interpretability research.
Key Achievements
- Master's degree (1.5 grade) in Cognitive Systems with thesis on benchmarking generative AI models
- Designed and implemented enterprise RAG systems enabling source-grounded question-answering
- Developed computer vision pipelines integrating YOLO, SAM, and PyTorch for industrial applications