Senior Machine Learning Engineer

Join our Team About this opportunity: Ericsson Enterprise Wireless Solutions (BEWS) is responsible for driving Ericsson's Enterprise Networking and Security business. Our expanding product portfolio covers wide area networks, local area networks, and enterprise security. We are the #1 global market leader in Wireless-WAN enterprise connectivity and are rapidly growing in enterprise Private 5G networks and Secure Access Services Edge (SASE) solutions. What Will You Do: Design, implement, and maintain data pipelines and ETL workflows for structured and unstructured data using scalable frameworks Build and automate ML pipelines for model training, deployment, monitoring, and retraining using MLOps best practices. Collaborate with data scientists to productionize ML models and ensure they meet performance, scalability, and reliability standards. Develop and manage feature stores, data validation, and metadata tracking to support reproducible ML experiments. Ensure high standards of code quality, documentation, and testing across all data and ML components. Implement data governance, lineage, and security practices in collaboration with platform and compliance teams. Monitor and optimize infrastructure costs and model inference performance. Evaluate new tools and frameworks to continuously improve the data and ML engineering ecosystem. Support debugging, performance tuning, and system maintenance for deployed models and pipelines What will you Bring: Strong proficiency in Python and SQL. Hands-on experience with data orchestration and workflow tools (e.g., Airflow, Kubeflow, Prefect). Solid understanding of machine learning model lifecycle, from experimentation to production deployment. Experience with cloud platforms (prefer AWS) and managed ML services (e.g., SageMaker). Knowledge of containerization and deployment technologies such as Docker and Kubernetes. Familiarity with CI/CD pipelines and version control using Git. Strong emphasis on code quality, modular design, and performance optimization. Able to build and deploy AI models into production with focus on scaling, monitoring and performance. Experience in automation and infrastructure management - AWS (EKS, S3, CloudWatch), Terraform, Infrastructure as Code (IaC), security and networking fundamentals Preferred Qualifications Bachelor's or Master's degree in Computer Science, Engineering, or related field. Experience with real-time data streaming (Kafka, Flink) and data Lakehouse architectures Knowledge of model monitoring and observability tools (Evidently AI, Prometheus, Grafana, etc.). Exposure to feature engineering frameworks and data contracts. Prior experience in telecom, enterprise AI, or AIOps domains is an advantage.