Ericsson’s R&D 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.