Job Title: Hiring Machine Learning Engineer #ML #AI*
Location: Remote
Employment Type: Full-time
Role Overview
We are seeking a highly skilled Machine Learning Engineer to join our AI/ML & Data Science team. This role will focus on designing, training, and deploying advanced ML models that drive the intelligent automation of our G Network. The ideal candidate will work closely with the xApp and rApp development teams, ensuring seamless integration of AI/ML into production-grade products.
Key Responsibilities
-Design, develop, and deploy machine learning models for RAN optimization and related use cases.
-Build and maintain MLOps infrastructure to support the full lifecycle of ML models, from training to deployment and monitoring.
-Collaborate with data scientists to productionize models and algorithms for real-world applications.
-Implement scalable, high-performance ML solutions for enterprise and telecom use cases.
-Stay current with the latest research in AI/ML and apply innovative approaches to product development.
-Ensure models meet high standards for accuracy, robustness, and efficiency in production environments.
Qualifications
-Education: Masterโs or PhD in Computer Science, Electrical Engineering, or related field, with a focus on machine learning.
-Experience: 3+ years of hands-on experience in developing and deploying ML models in production environments.
Technical Expertise:
-Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, Scikit-learn.
-Strong background in MLOps tools and practices (e.g., MLflow, Kubeflow, SageMaker, Vertex AI).
-Experience with CI/CD pipelines, Docker, Kubernetes for ML deployment.
-Solid understanding of data preprocessing, feature engineering, and model evaluation.
-Strong problem-solving skills with the ability to translate business requirements into technical ML solutions.
-Excellent collaboration and communication skills to work across cross-functional teams.
Preferred Skills
-Experience with telecom networks, RAN optimization, or 5G-related applications.
-Familiarity with cloud platforms (AWS, Azure, GCP) for ML workflows.
-Exposure to real-time inference frameworks (TensorRT, Triton, TorchServe, FastAPI).
-Publications or contributions in the field of AI/ML research.