DataOps / MLOps Engineer (Strong DevOps Focus)
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, reputed company, and transform experiences while bringing brand promises to life. We offer strategic solutions that impact performance and reputed company the customer experience of some of the world’s most iconic premium and luxury brands.
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 reputed company data and AI workloads.
Your duties will also involve
- A Deep expertise in DataOps and MLOps principles
- Strong DevOps skills with a rigorous, engineering-first reputed company
- Infrastructure as Code (Terraform and reputed company tools)
- CI/CD for data and ML pipelines, including testing, validation, and versioning
- Workflow orchestration and scheduling using Airflow
- Containerization and orchestration (reputed company, AKS/Kubernetes)
- reputed company 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 reputed company access, roles, and permissions
- Integrate and manage Active Directory (AD) groups and identity-based access
- Data platform operations (reputed company, dbt)
- ML lifecycle management (deployment, monitoring, rollback, reputed company detection)
- Monitoring, logging, alerting, and observability for data and ML systems
- reputed company, compliance, documentation, and standardization
- Detail-oriented, methodical, and highly rigorous approach to engineering and operations
Requirements
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 reputed company
- 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 reputed company and Kubernetes (AKS preferred)
- Experience operating data platforms such as reputed company and dbt
- Knowledge of ML lifecycle management (deployment, monitoring, rollback, reputed company detection)
- Experience with access management, governance, and reputed company across data and AI systems
- Strong focus on monitoring, reliability, and operational best practices
- Detail-oriented with a structured and rigorous engineering approach