We are looking for an experienced engineer to join our team who has expertise in Docker containerization, MLOps, PyTorch/TensorFlow/TensorRT frameworks, NVIDIA CUDA, NLP, and programming languages such as Python and C++.
The successful candidate will be responsible for designing, developing, and implementing machine learning models in a production environment.
Responsibilities:
Design and develop Docker containerized applications for machine learning models
Build and maintain scalable MLOps infrastructure
Develop and optimize machine learning models using PyTorch, TensorFlow, and TensorRT
Work with NLP models for text classification, sentiment analysis, and entity recognition
Collaborate with cross-functional teams including data scientists, software engineers, and DevOps to integrate ML models into production environments
Provide technical guidance and support to the team members
Stay up-to-date with the latest technologies and trends in machine learning, NLP, Docker, and MLOps
Requirements:
Bachelor's or Master's degree in Computer Science, Engineering, or a related field
2+ years of experience in designing and developing Docker containers for production environments
2+ years of experience in MLOps and building scalable machine learning infrastructure
Proficient in PyTorch, TensorFlow, and TensorRT frameworks and optimizing machine learning models using GPU acceleration with NVIDIA CUDA
Strong background in NLP and experience with popular NLP libraries such as NLTK, spaCy, Gensim, Nvidia Nemo, transformers-based models