AI/ML Engineer: Scalable Models in Production

Hewlett Packard Enterprise; Sant Cugat del Vallès; Presencial; EUR 55.000 - 90.000; Jornada completa

Descripción de la vacante

Hewlett Packard Enterprise is seeking an AI/ML Engineer to specialize in developing and deploying machine learning models that drive innovation and business value. You will research algorithms, run experiments, train and retrain systems, and ensure smooth production integration.

The role involves developing ML software solutions, tuning models, deploying to production, and collaborating with CI/CD and research teams to improve automated training techniques.

Formación

• Four-year or Graduate Degree in Computer Science, Statistics, Mathematics, Data Science, or related discipline.
• Typically 4-7 years of work experience in programming, ML, algorithms, or related field.

Responsabilidades

• Develops and programs integrated software solutions for ML model lifecycle.
• Trains and validates models using appropriate metrics and techniques.
• Optimizes hyperparameters to improve performance.
• Deploys ML models to production with scalability and reliability.
• Monitors and maintains deployed models and updates as needed.
• Designs ML systems and pipelines for scalable models.
• Collaborates with CI/CD teams for seamless deployment.
• Works with research and engineering teams to enhance training techniques.
• Applies ML and statistical modeling to business or research problems.

Conocimientos

Python

Java

C++

Machine Learning

Deep Learning

Data Science

Natural Language Processing

Software Engineering

Algorithms

Agile Methodology

Big Data

Distributed Systems

Communication

Educación

Bachelor's or higher in CS/Math/Stats/Data Science

Herramientas

AWS

Microsoft Azure

Apache Spark

PyTorch

TensorFlow

Scikit-learn

Descripción del empleo

Hewlett Packard Enterprise is seeking an AI/ML Engineer to specialize in developing and deploying machine learning models that drive innovation and business value. You will research algorithms, run experiments, train and retrain systems, and ensure smooth production integration.

The role involves developing ML software solutions, tuning models, deploying to production, and collaborating with CI/CD and research teams to improve automated training techniques.