Job
Description
Ericsson s R&D Data team is seeking a highly motivated and self-driven Machine Learning Engineer with experience in designing, developing and deploying machine learning models. Qualified candidates at the intersection of data science and software engineering, ensuring that AI models are efficient, scalable, and integrated into our production systems. You will work with a group of extremely high-performing engineers who design, implement, and support end-to-end SaaS solutions. You are adaptable and a flexible problem-solver with an algorithmic approach, technical expertise, engineering & analytics skills, and product sense to successfully pivot/context-switch amongst many projects with a variety of scale and complexity. Responsibilities: Design, develop, and optimize machine learning models for large-scale applications. Lead the end-to-end ML model lifecycle, including data preprocessing, feature engineering, training, tuning, and deployment. Architect, train, and deploy ML and deep learning models using AWS SageMaker and other cloud-based ML services. Guide and mentor junior ML engineers, establishing best practices for model development and productionization. Collaborate cross-functionally with data scientists, software engineers, and business stakeholders to align ML solutions with business objectives. Optimize model training and inference for performance, scalability, and cost efficiency. Stay up to date with the latest ML research and integrate cutting-edge techniques into production systems. Automate model retraining, monitoring, and deployment using MLOps and CI/CD pipelines. Work with distributed computing frameworks (e.g., Spark) for large-scale data processing and training. Requirements: 5+ years of experience in machine learning, deep learning, or AI-related fields. Bachelors, Masters, or PhD in Computer Science, AI, Data Science, or a related field. Strong programming skills in Python, Java, or C++ and expertise in ML frameworks like TensorFlow, PyTorch, or JAX. Extensive hands-on experience with AWS, including SageMaker, S3, Lambda, Step Functions, and other AI/ML services. Deep expertise in deep learning architectures (CNNs, RNNs, Transformers) and model training techniques. Strong knowledge of hyperparameter tuning, distributed training, and model optimization. Experience deploying and maintaining ML models in production environments at scale. Proficiency in MLOps best practices, including CI/CD pipelines, monitoring, and logging for ML models. Experience with big data processing frameworks like Spark is a plus. Strong problem-solving and leadership skills, with the ability to drive ML initiatives and mentor team members.
