Data Engineer • Mid-Level

Who you are

You have 3-5 years of experience combining Data Engineering and MLOps/DevOps, demonstrating a strong understanding of data pipelines and machine learning operations. Your technical expertise includes MLflow for managing experiments and model registries, ensuring reproducibility and traceability in your work.

You are proficient in building Infrastructure as Code (IaC) pipelines using Terraform or CloudFormation, allowing you to provision ML platform infrastructure efficiently. Your experience with containerization and orchestration tools like Docker and Kubernetes enhances your ability to deploy and manage applications in production environments.

You possess strong collaboration skills, working closely with Data Science and Data Engineering teams to align on product-oriented goals and service level objectives (SLOs). Your ability to document technical decisions and operational manuals ensures clarity and consistency across projects.

Desirable

Experience with feature stores and designing entity management systems is a plus, as is familiarity with monitoring data/model drift and performance in production. You are eager to learn and adapt to new technologies, contributing to a culture of continuous improvement and innovation.

What you'll do

In this role, you will design and maintain reproducible and versioned feature pipelines, orchestrating training processes that are traceable and well-documented. You will manage MLflow, overseeing experiments, model registries, and controlled deployments of models into production.

You will implement monitoring solutions for drift, data quality, and model performance, ensuring that deployed models meet the required standards. Automating the promotion of models between environments (development, QA, production) with appropriate gate checks and approvals will be a key responsibility.

You will define repository templates with standard CI/CD practices, testing, and conventions to streamline development processes. Your work will involve ensuring professional packaging of libraries and models, utilizing tools like wheel and Poetry for semantic versioning.

Collaboration is essential in this role; you will work closely with Data Science and Data Engineering teams to align on product roadmaps and SLOs. Documenting technical decisions, data contracts, and operational manuals will be part of your daily tasks, contributing to the overall efficiency and clarity of the team’s work.

What we offer

At NEORIS, you will be part of a multicultural team of over 4,000 professionals across 11 countries, fostering a startup culture that encourages innovation and continuous learning. We offer private medical insurance and opportunities for courses and certifications to support your professional growth.

Join us in our mission to help companies transition into the future through digital acceleration, leveraging your skills to create high-value solutions for our clients. We encourage you to apply even if your experience doesn't match every requirement, as we value diverse perspectives and backgrounds.