You will focus on model optimization, software integration, and deployment, ensuring that AI solutions perform efficiently in real-world high-throughput environments. This role requires strong software engineering skills combined with a deep understanding of ML workflows and performance considerations.
Key Responsibilities
Integrate AI/ML models developed by research teams into production systems
Optimize model performance for runtime, memory, and inference efficiency (e.g., TensorRT, ONNX)
Adapt research-grade code into scalable, maintainable, and production-ready software
Collaborate with Algorithms, Software, and System teams to ensure seamless end-to-end integration
Improve inference pipelines and system performance in real-world environments
Develop and maintain infrastructure for deploying and monitoring AI models in production
Requirements
B.Sc. in Computer Science, Electrical Engineering, or a related technical field from a recognized university
3+ years of hands-on experience in Machine Learning and software infrastructure development
Strong programming skills in Python and experience with PyTorch
Experience with software development in C++ or C#
Experience working in Linux and Windows environments, including version control (Git)
Strong understanding of multithreading and performance optimization
Experience implementing and integrating algorithms into production systems
Strong analytical and problem-solving skills
Excellent collaboration, communication, and documentation skills
Self-motivated, with the ability to work independently and drive tasks to completion
Advantages
Experience with computer vision pipelines and image data processing
Hands-on experience with TensorRT and ONNX
Familiarity with CUDA and GPU-level optimization
Experience deploying ML models in production environments
Familiarity with CI/CD pipelines
Experience working with MLOps tools (e.g., MLflow, Kubeflow, or similar)
Experience with generative models (e.g., diffusion models)
