Senior Machine Learning Engineer

Avaron AB is a growing consultancy focused on technology, finance, and business support. We match your expertise with the market's most interesting assignments, offering a platform where your professional development is central.

You will join a team building and operating customer-facing recommendation and personalization solutions. The environment is centered around Google Cloud Platform (GCP) and modern data/ML engineering practices, with a strong focus on reliable pipelines, high code quality, and scalable delivery.

• Develop and maintain ML/data pipelines using Vertex AI pipelines/Kubeflow and orchestration tooling (Cloud Composer/Airflow).
• Work with BigQuery and write efficient SQL to support analytics and ML use cases.
• Build and maintain Python codebases with strong engineering standards (OOP, linting/type checks, testing, and CI/CD).
• Collaborate with stakeholders and engineers to design robust data models and system designs.
• Contribute to code reviews and continuous improvement of engineering practices.
• Manage access and permissions in GCP (IAM, service accounts) and use Data Catalog where relevant.
• Support infrastructure and deployment practices aligned with Infrastructure as Code principles.

• Hands-on experience with GCP, including Vertex AI pipelines/Kubeflow pipelines.
• Experience with BigQuery and strong SQL skills.
• Experience with Cloud Composer/Airflow.
• Experience with IAM and service accounts.
• Experience with Data Catalog.
• Understanding of Infrastructure as Code concepts.
• Strong Python skills, including OOP and best practices.
• Experience with code quality tooling such as flake8, mypy, black, SonarQube, and pre-commit.
• Strong testing practices (unit and end-to-end) and familiarity with Pytest, fixtures, and unittest.
• Deep understanding of dbt (preferably in a GCP context).
• Good Unix and shell skills.
• Git proficiency (PR workflow and resolving merge conflicts).
• Ability to create CI/CD pipelines using GitHub Actions.
• Strong Docker knowledge.
• Understanding of data modeling and system design.

• Experience with Dataflow.
• Experience with Kubernetes.
• Experience building high-availability APIs.
• Background in machine learning–based recommendation systems and personalization.

Selections are made on an ongoing basis, so we recommend that you apply as soon as possible.