Ajinkya Indulkar
Head Of Engineering at Sensity
Amsterdam, North Holland, Netherlands
Summary
🤩
Rockstar🎓
Top SchoolAjinkya Indulkar is Head of Engineering at Sensity, bringing eight years of experience building production ML systems with a five-year focus in the ML industry. He specializes in end-to-end anti-fraud detection and generative AI solutions, leading technical strategy and engineering teams to operationalize deepfake detection at scale. His work spans hands-on model development, performance optimizations (e.g., accelerating video reverse-mapping with kornia), and improving tooling and configuration for widely used research toolkits. A cum laude MSc in Artificial Intelligence underpins his research-driven approach, which began with a lip-sync deepfake thesis and includes neuromorphic spiking-network exploration. Based in Amsterdam, he combines startup agility with disciplined engineering practices to translate cutting-edge research into reliable anti-fraud products.
8 years of coding experience
6 years of employment as a software developer
Master of Science - MS (Cum Laude) Artificial Intelligence, Master of Science - MS (Cum Laude) Artificial Intelligence at Radboud UniversityBachelor of Technology - BTech Electronics and Communications, Bachelor of Technology - BTech Electronics and Communications at Manipal Institute of Technology
English
Stackoverflow
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0reached
0answers
0questions
Github Skills (10)
computer-vision10
pytorch10
deep-learning10
python10
image-processing9
machine-learning9
video-processing9
google-colab7
configuration-management7
cicd4
Programming languages (7)
Github contributions (5)
sensity-ai/dot
Jun 2022 - Aug 2022
The Deepfake Offensive Toolkit
Role in this project:
ML Engineer
ML EngineerContributions:1 release, 49 reviews, 14 commits in 1 month
Contributions summary:Ajinkya primarily focused on enhancing the `dot` toolkit, specifically related to deepfake and video manipulation capabilities. They added a new demo notebook showcasing the SimSwap feature and integrated the kornia library to accelerate the `reverse2original` function, indicating a focus on optimizing performance. Furthermore, they worked on adding configuration file support and refining camera-related functionalities, demonstrating a broad involvement in improving the toolkit's features and usability.
deep-learningoffensivemachine-learningdeepfake
ajndkr/lanarky
Apr 2023 - Jul 2024
The web framework for building LLM microservices
Contributions:50 releases, 38 reviews, 119 PRs in 1 year 3 months
fastapillmopsmicroservicespython3
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