DataOps / MLOps Engineer (Strong DevOps Focus)

CXG; Paris; Hybride; EUR 67 000 - 85 000; Plein temps

Résumé du poste

A global business serving luxury brands seeks a remote DataOps / MLOps Engineer with strong DevOps skills. You will design and operate reliable DataOps platforms, manage ML pipelines, and ensure adherence to best practices in automation and security. Ideal candidates will have 2-3 years of experience in DataOps/MLOps, be detail-oriented, and possess a strong engineering background. This role offers the opportunity to work with some of the world's most iconic brands.

Qualifications

• 2 to 3 years of experience in DataOps and MLOps in production environments.
• Strong hands-on experience in DevOps engineering with an automation-first mindset.
• Detail-oriented and methodical approach to engineering and operations.

Responsabilités

• Designs, builds, and operates DataOps and MLOps platforms.
• Owns infrastructure, data, and ML production pipelines end to end.
• Acts as quality and reliability gatekeeper for all data and AI workloads.

Connaissances

DataOps expertise

MLOps expertise

DevOps skills

Infrastructure as Code (Terraform)

CI/CD for data and ML pipelines

Workflow orchestration (Apache Airflow)

Containerization (Docker, Kubernetes)

Data platform operations (Snowflake, dbt)

Outils

Docker

Kubernetes

Terraform

Apache Airflow

Snowflake

dbt

Description du poste

Overview

We are growing! We are currently looking to hire a DataOps / MLOps Engineer (Strong DevOps Focus) to work with us remotely

Who we are:

Founded in 2006, we’re proud to be a global business. From Shanghai to Paris, we have 12 offices and operate across four continents in 70 countries. We are home to over 250 professionals from around the world, working together to serve more than 230 luxury clients.

At CXG, we love to evolve, elevate, and transform experiences while bringing brand promises to life. We offer strategic solutions that impact performance and elevate the customer experience of some of the world’s most iconic premium and luxury brands.

Responsibilities

What you will be doing:

Designs, builds, and operates highly reliable, secure, and scalable DataOps and MLOps platforms with strong engineering rigor. Owns infrastructure, data, and ML production pipelines end to end, enforcing automation, testing, governance, and operational best practices. Acts as the quality and reliability gatekeeper for all data and AI workloads.

• A Deep expertise in DataOps and MLOps principles
• Strong DevOps skills with a rigorous, engineering-first mindset
• Infrastructure as Code (Terraform and related tools)
• CI/CD for data and ML pipelines, including testing, validation, and versioning
• Workflow orchestration and scheduling using Airflow
• Containerization and orchestration (Docker, AKS/Kubernetes)
• Cloud platform management (with focus on AKS and supporting services)
• Governance and access management across the data & AI ecosystem
• Manage repository access for Data Engineers, Analysts, and Scientists
• Control and audit Snowflake access, roles, and permissions
• Integrate and manage Active Directory (AD) groups and identity-based access
• Data platform operations (Snowflake, dbt)
• ML lifecycle management (deployment, monitoring, rollback, drift detection)
• Monitoring, logging, alerting, and observability for data and ML systems
• Security, compliance, documentation, and standardization
• Detail-oriented, methodical, and highly rigorous approach to engineering and operations

Qualifications

What you will bring along:

• The ideal experience range for this role is 2 to 3 years
• Strong hands-on experience in DataOps and MLOps in production environments
• Solid DevOps engineering background with an automation-first mindset
• Experience with Infrastructure as Code using Terraform
• Expertise in CI/CD for data and ML pipelines, including testing and versioning
• Workflow orchestration using Apache Airflow
• Containerization and orchestration with Docker and Kubernetes (AKS preferred)
• Experience operating data platforms such as Snowflake and dbt
• Knowledge of ML lifecycle management (deployment, monitoring, rollback, drift detection)
• Experience with access management, governance, and security across data and AI systems
• Strong focus on monitoring, reliability, and operational best practices
• Detail-oriented with a structured and rigorous engineering approach