Responsibilities
• Writing reusable, testable, and efficient production code in Machine and Deep Learning
pipelines for Computer Vision
• Design and implementation of low-latency, high-availability, and performant applications
• Assess and prioritize feature requests
• Coordinate with internal teams to understand user requirements and provide technical solutions
• Integration of user-facing elements developed by front-end developers with server-side logic
• Integration of data storage solutions like databases, key-value stores, blob stores, etc.
Experience- 2-5 years
Location: Technopark, Thiruvanathapuram
Work Mode: Full Time and work from Office
Skills And Qualifications
• Any one of the following combinations:
• (Python and C++) OR (Python and Rust) OR (Python and Java) OR (Python and Kotlin):
(should have developed computer vision models with any of Jax, Tensorflow 2 or
PyTorch. Both programming languages are must, and Python with C++ or Python with
Rust streams will be ranked higher.)
• (Good to have): Deployed models and Computer Vision data processing models and
pipelines with ONNX Runtime, TensorRT and DeepStream Runtime
• CUDA-C/OpenCL-C – big plus!
• Demonstrable Image Processing and Computer Vision problem-solving skills, understanding of
modern machine learning methods and deep learning architectures and paradigms, e.g., object
detection and segmentation, Image classification with various Deep Neural Networks,
conceptual understanding of zero-shot architectures in various computer vision tasks.
Candidates may be asked to code specific DNN architectures and various computer vision and
image processing routines during the interview process.
• Proficient with Linux Command Line (Must, since most of the work is on remotely connected
edge AI/IoT devices)
• Understanding of fundamental design principles behind a scalable application
• Understands and is able to write and consume or learn writing HTTP REST and Streaming APIs
using Python, java or Rust stacks.• Understanding of the differences between multiple delivery platforms, such as mobile vs
desktop, and optimizing output to match the specific platform