DataOps / MLOps Engineer (Strong DevOps Focus)

CXG•Lebanon, PA

5h•Remote

About The Position

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. 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.

Requirements

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
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

Responsibilities

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.
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