Description

In this role, you will design and maintain data pipelines while collaborating with data scientists to implement machine learning models. Your work will focus on optimizing data architectures for AI applications in energy storage, ensuring data quality and security.

Requirements

• •M.Sc. in Computer Science, Data Engineering, AI, Energy Technology, or related fields
• •Master's with extensive experience in Computer Science, Data Engineering, AI/ML
• •Solid programming skills in Python
• •Experience using PySpark, SQL, and Linux-based environments
• •Strong understanding of AI/ML concepts
• •Practical experience integrating ML models into production systems
• •Familiarity with tools like MLflow, Kubeflow, or Airflow
• •Experience working with data lakehouses, streaming architectures, and data governance frameworks
• •Good understanding of energy storage systems
• •Familiarity with battery-related algorithms such as SoC, SoH, RUL prediction, and fault detection
• •Prior work or research in battery management systems or energy storage algorithms
• •Experience with vector databases (e.g., FAISS, Pinecone) and LLM-based applications
• •Familiarity with modern MLOps and DevOps best practices
• •Contributions to open-source AI or data engineering projects

Tasks

• •Design and build scalable data pipelines for energy-related data
• •Maintain reliable data infrastructure for structured and unstructured data
• •Collaborate with data scientists to operationalize machine learning models
• •Process large-scale data using distributed frameworks like Spark and Kafka
• •Implement and optimize data architectures for AI applications
• •Apply AI/ML techniques to Energy Storage System applications
• •Contribute to model experimentation tracking and autoML workflows
• •Ensure data integrity, quality, and security throughout the pipeline
• •Assist in designing AI-infused data platforms

Tools & Technologies

Benefits