Aditya Digala
Member Of Technical Staff - AI at micro1
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolAditya Digala is an AI engineer and data scientist with 8 years of experience building production-ready ML systems, currently shaping agentic GenAI products as Member of Technical Staff - AI in Boston. He combines end-to-end MLOps, vision-language fine-tuning, and RAG/agent workflows—having deployed YOLOv8 detectors, fine-tuned LLaVA NeXT at scale, and built privacy-aware data pipelines for insurance use cases. A hands-on practitioner who also teaches, he co-developed Northeastern’s first Generative AI course and led TAs to deliver practical projects like chatbots and AI agents. Aditya is an active open-source contributor (notably extending the Face-X repo with practical CV utilities) and brings a maker’s instinct—equally comfortable experimenting with novel GenAI architectures and hardening them for enterprise reliability.
8 years of coding experience
2 years of employment as a software developer
Jawaharlal Nehru Technological University HyderabadDAV Public SchoolMasters Applied machine intelligence, Masters Applied machine intelligence at Northeastern University
Stackoverflow
351reputation
49kreached
2answers
20questions
Github Skills (20)
algorithm10
face-detection10
algorithms10
opencv10
python10
tensorflow10
computer-vision10
facial-recognition7
face-recognition7
flask6
keras6
object-detection-api6
deep-learning6
gpu6
machine-learning6
Programming languages (7)
Github contributions (5)
akshitagupta15june/Face-X
Mar 2021 - Apr 2021
Demonstration of different algorithms and operations on faces. Star the repo⭐
Role in this project:
ML Engineer
ML EngineerContributions:15 commits, 4 PRs, 14 comments in 23 days
Contributions summary:Aditya primarily contributed to the development and integration of computer vision algorithms for face-related tasks within the `face-x` repository. They implemented functionalities such as cartoonification, face detection, background blurring, ghost image generation, mosaic effects, and pencil sketch transformations. The user also added a face mask detection feature, encompassing both image and video processing using pre-trained models and libraries like OpenCV and TensorFlow. These contributions demonstrate a focus on practical application and extension of the project's face-related capabilities.
face-recognitionface-recognition-applicationrecognition-algorithmsface-detectionface-recognition-database
AdityaNikhil/Hand-Pose-Classification
Nov 2020 - Dec 2020
Contributions:32 commits in 29 days
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