Applied AI – Graduate Intern

EnerSys

Intern contributing to design, development, and evaluation of AI applications in energy. Engaging with projects on intelligent workflow automation and LLM applications in various contexts.

Posted 4/22/2026internshipAnytown • Alabama, Arizona, California, Colorado, Connecticut, Florida, Hawaii, Idaho, Iowa, Kentucky, Louisiana, Maine, Maryland, Massachusetts, Minnesota, Missouri, Montana, Nevada, New Hampshire, New Jersey, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, Rhode Island, South Carolina, South Dakota, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, Wisconsin, Wyoming • 🇺🇸 United StatesEntry Level💰 $35 - $40 per hourWebsite

Tech Stack

Tools & technologies

NumpyPandasPythonPyTorch

About the role

Key responsibilities & impact

Design and implement agentic AI architectures, including multi-agent workflows, tool-calling systems, memory and state management, and orchestration logic for complex multi-step tasks.
Develop and evaluate retrieval-augmented generation (RAG) pipelines, with attention to retrieval strategy, document chunking and indexing, embedding model selection, re-ranking, and end-to-end evaluation.
Contribute to LLM evaluation and validation frameworks — defining test coverage, constructing evaluation datasets, assessing output reliability, and identifying failure modes through structured testing and adversarial analysis.
Conduct prompt engineering and instruction design for domain-specific tasks, and support experimentation with parameter-efficient fine-tuning approaches where applicable.
Perform model benchmarking and comparative analysis, including evaluation of commercial and open-source LLMs for specific task types, latency and cost tradeoffs, and domain adaptation requirements.
Support integration of LLM and agentic components with broader system architectures, including data pipelines, APIs, and domain-specific tooling.
Contribute to data preparation and preprocessing workflows for structured and unstructured industrial datasets, including cleaning, transformation, and schema design.
Engage in the full engineering rigor expected of production AI/ML systems — including unit and integration testing, model verification and validation, experiment evaluation, simulation workflows, data and output visualization, and technical documentation and reporting — as continuous activities throughout the project lifecycle.

Requirements

What you’ll need

Currently enrolled in a Master's or PhD program in Computer Science, Artificial Intelligence, Data Science, Electrical Engineering, or a closely related discipline — or recently graduated from such a program.
Strong theoretical foundation in machine learning and deep learning, with the ability to reason about model behavior, generalization, and failure modes.
Demonstrated hands-on experience building LLM-based applications — including at least one of the following: agentic systems, RAG pipelines, structured output generation, or tool-augmented language models.
Proficiency in Python; fluency with ML frameworks, particularly PyTorch.
Strong data handling and analysis skills — experience working with complex, real-world datasets using pandas, NumPy, or equivalent tools.
Ability to design and execute rigorous experiments, interpret results critically, and communicate findings clearly in written and verbal form.

Benefits

Comp & perks

Paid time off plus paid holidays
Medical/dental/vision insurance plan
Life insurance, short/long term disability, tuition reimbursement, flex spending, and employee stock purchase plan
401K plan

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

agentic AI architecturesmulti-agent workflowsretrieval-augmented generation (RAG) pipelinesprompt engineeringmodel benchmarkingmachine learningdeep learningPythonPyTorchdata preparation

Soft Skills

critical thinkingcommunicationexperiment designdata analysis