AI/ML Engineering Team

EnerSys is a global leader in stored energy solutions for industrial applications. The AI/ML Engineering team builds intelligent systems across energy management, asset health monitoring, digital twin simulation, and manufacturing optimization. Across all of these initiatives, robust and well-engineered data infrastructure is a foundational requirement. Data Engineering at EnerSys operates at the interface of data systems and AI/ML development, with direct responsibility for the quality, availability, and structure of data that feeds production models and research workflows.

This role is not a traditional reporting or analytics function. It requires both the technical rigor of a data engineer and the domain fluency of someone who understands machine learning systems — how models consume data, where pipeline design decisions affect model performance, and how data quality issues manifest downstream in production.

We offer a 6-month internship.  

Position Summary

The Data Engineering Intern will design and build data infrastructure that supports active AI/ML projects across EnerSys's portfolio — including energy management systems, digital twin simulation, predictive maintenance, and manufacturing optimization. The intern will contribute to ingestion pipelines, feature engineering workflows, data quality frameworks, and analytical tooling, working in close collaboration with AI/ML engineers and researchers throughout. A strong and demonstrated understanding of machine learning concepts and workflows is required; the intern will be expected to make data design decisions with the downstream model lifecycle explicitly in mind.

Essential Duties and Responsibilities

Design and implement data ingestion, transformation, and validation pipelines for structured, semi-structured, and time-series data originating from BESS telemetry, industrial chargers, sensor networks, and manufacturing systems.

• Develop data quality and monitoring frameworks — including schema validation, completeness checks, outlier flagging, and drift detection — for both ML training pipelines and real-time inference inputs.

• Build and maintain feature engineering pipelines in collaboration with AI/ML engineers, with attention to temporal feature construction, normalization strategies, and feature store design.

• Design data models and storage schemas optimized for time-series retrieval, ML consumption, and cross-project reuse; evaluate tradeoffs across storage formats and database architectures including analytical, vector, and graph database systems.

• Contribute to streaming data pipeline development, supporting real-time ingestion and event-driven architectures for high-throughput industrial data sources.

• Contribute to synthetic data generation and simulation data workflows supporting the digital twin platform and EMS development.

• Develop exploratory analysis and visualization outputs that provide interpretable views of data quality, coverage, and distributional properties for research and project teams.

• Engage in the full engineering rigor expected of production AI/ML data systems — including pipeline testing and validation, data contract verification, simulation and load testing, output visualization, and thorough technical documentation and reporting — as continuous activities throughout the project lifecycle.

Required Qualifications

Currently enrolled in a Master's or PhD program in Computer Science, Data Science, Electrical Engineering, or a closely related field — or recently graduated from such a program.

• Strong proficiency in Python for data manipulation, transformation, and pipeline development.

• Solid experience with SQL and relational or time-series data systems.

• Demonstrated understanding of machine learning workflows — including training data construction, feature engineering, inference pipeline design, and the downstream impact of data quality on model behavior.

• Hands-on experience handling large, complex, real-world datasets — including missing data, schema inconsistencies, temporal alignment challenges, and high-cardinality categorical variables.

• Strong data visualization skills with libraries such as matplotlib, seaborn, or Plotly; ability to produce analysis outputs that are clear, informative, and reproducible

Preferred Qualifications

Familiarity with cloud data platforms, particularly Microsoft Azure (e.g., Azure Databricks, Azure Data Factory, Azure Data Lake, or Event Hubs); equivalent experience with other major cloud providers is also relevant.

• Exposure to large-scale or streaming data concepts, including real-time ingestion pipelines, event-driven architectures, or distributed data processing frameworks (e.g., Spark or similar).

• Experience with relational or analytical databases (e.g., PostgreSQL or equivalent); awareness of modern storage formats or database categories — including columnar, vector, or graph databases — is a plus.

• Hands-on ML experience, including model training, feature engineering, or experiment tracking (e.g., MLflow or similar tools); understanding of how data pipeline design impacts model performance.

• Interest in or prior exposure to time-series sensor data, industrial telemetry, battery systems, or energy infrastructure is a plus.