Machine Learning Engineering Intern

Sustainability that means business

Who we are:

Sustainability software specialist, AMCS, is headquartered in Ireland, with offices in Europe, the USA, and Australasia. With over 1,300 highly-skilled employees across 22 countries, we specialize in delivering technology solutions to facilitate a carbon neutral future.

What we do:

Our innovative SaaS solutions increase efficiency and boost sustainability in resource-intensive industries. Over 5,000 customers across 23 countries already benefit from our Performance Sustainability software, ensuring we deliver practical solutions for improved profitability and environmental resilience across the globe.

Our people

AMCS offers team members more than just a job, but an opportunity to map out a career with a company that is growing, evolving and setting out new ways of working that are having a positive impact on the world around us. AMCS was established in Ireland and holds onto those local roots and ‘start-up’ mentality with a culture of connection. Connection to our work, our customers, our colleagues and our community that creates a working environment that fosters openness, collaboration and creativity.

As a Machine Learning Engineering Intern, you will contribute to the design, development, and deployment of machine learning models and data pipelines. You’ll support the team in data preparation, model training, and evaluation, while learning best practices for ML in production. This is a great opportunity for someone eager to learn about ML workflows, data engineering, and software development in an industry setting.

Responsibilities:

Assist in data cleaning, preprocessing, and exploratory data analysis to prepare data for model training.

Develop, train, and fine-tune machine learning models using frameworks/tools like Python/PySpark, Python, TensorFlow, PyTorch, and Scikit-Learn on Azure Databricks

Build, maintain, and optimize data pipelines for seamless data flow and model integration Azure Databricks using PySpark/Python and Spark SQL

Implement and evaluate ML models, tracking performance and refining based on feedback.

Collaborate with team members to integrate models into production environments.

Document processes, code, and findings, contributing to team knowledge sharing.

Stay current with the latest ML trends and research to support ongoing projects.

Requirements:

Currently pursuing a bachelor's degree (final year), Master's, or PhD in Computer Science, Data Science, Engineering, or a related field

Hands-on experience with ML frameworks (TensorFlow, PyTorch, or Scikit-Learn)

Understanding of fundamental machine learning and deep learning concepts, algorithms, and data structures

Familiarity with Large Language Models (LLMs) and attention-based models

Experience with building, managing, and optimizing data pipelines.

Strong analytical skills and problem-solving abilities

Excellent communication and teamwork skills
Nice-to-Have:

Experience with Databricks, PySpark, Spark SQL, and Apache Spark

Familiarity with MLOps best practices

Proficiency with Power BI for data visualization

Experience with development of Retrieval-Augmented Generation (RAG) models

Familiarity with LlamaIndex and LangChain for building LLM-powered applications.
What You’ll Gain:

Hands-on experience in real-world machine learning projects and data pipeline development

Mentorship and support from industry professionals

Exposure to cutting-edge platforms and technologies