Principal Engineer

Join our Team
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 maintainhigh-performance, scalable data pipelinesfor ML applications.
• Ensure data availability, reliability, and quality for AI-driven applications.
• Work withstreaming and batch processing frameworks(e.g., Spark, Kafka, Flink).
• Optimize data storage and retrieval for large-scale ML workloads.
Architecture & Leadership
• Define theAI and data strategy, ensuring alignment with business goals.
• Drive best practices forscalability, reliability, and securityin ML & data infrastructure.
• Mentor engineers and foster a culture of innovation and excellence.
• Collaborate cross-functionally withsoftware 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+ yearsindata engineering, ML engineering, or related fields.
• Proven experiencedeploying ML models in productionat scale.
• Strong understanding ofdata architectures for AI-driven applications.
• Experience withmicroservices and API-driven architectures.
• Demonstrated leadership inAI/ML strategy and best practices.

Preferred Qualifications
• Experience withLLMs and generative AIin production.
• Knowledge ofnetworking and distributed systems(ideal for router-related use cases).
• Contributions toopen-source ML or data engineering projects.