Job
Description
Join our Team Ericsson’s R&D Data team is seeking a motivated and self-driven Machine Learning Engineer with a strong foundation in designing, developing, and deploying machine learning models. This role sits at the intersection of data science, software engineering, and data engineering—focused on building scalable AI solutions that integrate into real-world production systems. You'll join a team of high-performing engineers building end-to-end SaaS solutions, where adaptability, a data-centric mindset, and strong technical skills are key to success. Responsibilities: Assist in designing, developing, and optimizing machine learning models for real-world applications. Contribute to the ML model lifecycle: data preprocessing, feature engineering, training, evaluation, and deployment. Work with AWS SageMaker and other cloud-based tools to train and deploy models. Collaborate with data engineers to build reliable and scalable data pipelines for ML training and inference. Work with event streaming platforms (e.g., Amazon MSK or equivalent) for real-time data ingestion and processing. Partner with senior engineers and data scientists to align ML solutions with business and product goals. Help integrate models into production environments and monitor their performance. Support automation of model retraining and deployment using MLOps tools and CI/CD pipelines. Stay up to date with the latest ML and data engineering technologies to bring best practices to the team. Requirements: 5+ years of experience in machine learning, deep learning, or AI-related fields. Bachelor's degree in Computer Science, AI, Data Science, or a related field. Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn. Hands-on experience with cloud services (e.g., AWS SageMaker, S3, Lambda) is a plus. Familiarity with ML model architectures (e.g., CNNs, RNNs, Transformers) and training techniques. Exposure to MLOps practices (e.g., CI/CD, monitoring, model versioning). Experience working with real-time data streaming platforms (e.g., Amazon MSK, or similar). Familiarity with data engineering tools and practices (e.g., building ETL pipelines, using Spark, Airflow, or similar). Strong analytical thinking and a collaborative mindset with a desire to grow technically. Show more Show less
