Ericsson s RD Data team is seeking a highly motivated and self-driven Principal Machine Learning Data Engineer with experience in designing, developing and deploying machine learning models
along with the ability to build and maintain highly scalable data pipelines. 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.
Key Responsibilities
Machine Learning Engineering
Architect, build, and deploy scalable machine learning models in production environments.
Optimize ML models for performance, efficiency, and cost-effectiveness.
Implement MLOps best practices for CI/CD, monitoring, and retraining of models.
Collaborate with data scientists to transition models from research to production.
Data Engineering
Design and maintain high-performance, scalable data pipelines for ML applications.
Ensure data availability, reliability, and quality for AI-driven applications.
Work with streaming and batch processing frameworks (e.g., Spark, Kafka, Flink).
Optimize data storage and retrieval for large-scale ML workloads.
Architecture Leadership
Define the AI and data strategy , ensuring alignment with business goals.
Drive best practices for scalability, reliability, and security in ML data infrastructure.
Mentor engineers and foster a culture of innovation and excellence.
Collaborate cross-functionally with software engineers, DevOps, and product teams .
RequirementsTechnical Skills
ML AI Frameworks: TensorFlow, PyTorch, Scikit-learn
Big Data Streaming: Apache Spark, Kafka, Flink, Snowflake, Delta Lake
Cloud Infrastructure: AWS, GCP, or Azure (EC2, S3, Lambda, SageMaker, Databricks)
Programming Languages: Python (preferred), Scala, Java, SQL
MLOps DevOps: Kubernetes, Docker, CI/CD, MLflow, Airflow, Feature Stores
Data Engineering: ETL, Data Warehousing, Data Lakes, Distributed Computing
Experience Qualifications
10+ years in data engineering, ML engineering, or related fields .
Proven experience deploying ML models in production at scale.
Strong understanding of data architectures for AI-driven applications .
Experience with microservices and API-driven architectures .
Demonstrated leadership in AI/ML strategy and best practices .
Preferred Qualifications
Experience with LLMs and generative AI in production.
Knowledge of networking and distributed systems (ideal for router-related use cases).
Contributions to open-source ML or data engineering projects .
