Luca Antiga
Chief Technology Officer at Lightning AI
Lombardy, Italy
Summary
🤩
Rockstar🎓
Top SchoolLuca Antiga is a seasoned technology leader and CTO with 18 years of experience building AI-first, cloud-native systems from research prototypes to production. He co-founded multiple startups (Orobix, Orobix Life, Tensorwerk) and led engineering and product strategy at Lightning AI, where his work spanned PyTorch integrations, distributed training tooling, and improving Lightning App database and tracing infrastructure. A hands-on engineer by training (PhD in Biomedical Engineering, Politecnico di Milano), Luca contributes to widely used open-source scientific and ML projects such as ITK and PyTorch Lightning, and has implemented low-level components like Redis module tensor APIs and mesh readers for VMTK. He bridges deep technical expertise in medical imaging and computational geometry with product leadership, often tackling subtle correctness and performance issues in complex systems. Based in Lombardy, Italy, he pairs academic rigor with startup execution and a track record of shipping robust back-end and DevOps solutions that accelerate ML workflows.
18 years of coding experience
17 years of employment as a software developer
Electronic Engineering, Electronic Engineering at Università degli Studi di BresciaPhD, Biomedical Engineering, PhD, Biomedical Engineering at Politecnico di Milano
Github Skills (37)
image-analysis10
pytorch10
c-language10
python10
back-end-development10
redis10
testing10
open-source10
machine-learning10
c1110
data-structure10
c1710
api10
tensorflow10
deep-learning10
Programming languages (13)
Github contributions (5)
RedisAI/redis-inference-optimization
Sep 2016 - Jun 2021
A Redis module for serving tensors and executing deep learning graphs
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:1 release, 109 reviews, 517 commits in 4 years 10 months
Contributions summary:Luca implemented the tensor and run API, adding functionality to the Redis module for deep learning graphs. They developed functions to handle different data types, shapes, and data sizes of tensors. The user's work included the creation of a graph Redis data type and the refactoring of the run command for improved functionality, including the handling of input and output keys and the integration of batching features.
pytorchredisservingserving-tensorsredis-module
vmtk/vmtk
Feb 2011 - Apr 2020
the Vascular Modeling Toolkit
Role in this project:Back-end Developer
Back-end DeveloperContributions:1 release, 516 commits, 37 PRs in 9 years 3 months
Contributions summary:Luca primarily contributed to the Vascular Modeling Toolkit (VMTK) by implementing and enhancing file readers and writers. Their work included adding a TetGen reader and writer for unstructured grid data and a PLY reader/writer. The user also made improvements to the Dolfin and ARCH network writers and created scripts for surface and mesh manipulation and analysis.
deep-learningvolume-renderingelectrophysiologyvascularmodeling
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