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Detection Engineer

  • Detection Engineer job type Posted on: Oct 14, 2026
  • Detection Engineer experience level Openkyber
  • Detection Engineer employment type Atlanta, Georgia
  • Detection Engineer employment type Onsite
  • Detection Engineer salary Full-time

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Job Title :

Detection Engineer

Job Type :

Full-Time

Job Location :

Atlanta Georgia United States

Remote :

No

Jobcon Logo Job Description :

Hi Our client is looking for a Lead MLOps Engineer project in Dallas, TX . Below are the detailed requirements. Job Title : Lead MLOps Engineer - Fraud Detection Platform Location : Dallas, TX Required Skills : MLOps, Google Cloud Platform, SRE, Agentic AI Job description: Bachelor's degree in computer science, Information Technology, or a related field 8 12 years of experience in MLOps, DevOps, or ML Platform Engineering. Strong experience building enterprise-scale ML deployment platforms. Expertise in Google Cloud Platform services and cloud-native AI infrastructure. Hands-on experience with CI/CD pipelines for ML model lifecycle management. Experience designing model registries and release automation frameworks. Strong Kubernetes and container orchestration experience. Expertise in monitoring, observability, and production support. Experience implementing governance, compliance, and security controls. Knowledge of Agentic AI architectures and AI operational frameworks. Experience supporting high-volume fraud detection or risk platforms preferred.

For applications and inquiries,

Jobcon Logo Position Details

Posted:

Oct 14, 2026

Reference Number:

93417-38381

Employment:

Full-time

City:

Atlanta

Job Origin:

CIEPAL_ORGANIC_FEED

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Hi Our client is looking for a Lead MLOps Engineer project in Dallas, TX . Below are the detailed requirements. Job Title : Lead MLOps Engineer - Fraud Detection Platform Location : Dallas, TX Required Skills : MLOps, Google Cloud Platform, SRE, Agentic AI Job description: Bachelor's degree in computer science, Information Technology, or a related field 8 12 years of experience in MLOps, DevOps, or ML Platform Engineering. Strong experience building enterprise-scale ML deployment platforms. Expertise in Google Cloud Platform services and cloud-native AI infrastructure. Hands-on experience with CI/CD pipelines for ML model lifecycle management. Experience designing model registries and release automation frameworks. Strong Kubernetes and container orchestration experience. Expertise in monitoring, observability, and production support. Experience implementing governance, compliance, and security controls. Knowledge of Agentic AI architectures and AI operational frameworks. Experience supporting high-volume fraud detection or risk platforms preferred.

For applications and inquiries,

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