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Lead MLOps Engineer - Fraud Detection Platform

Lorven Technologies Inc
Dallas, TX, United States
21 days ago
Apply on www.dice.com
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
8 years minimum
Working hours
Regular working hours
Job source
www.dice.com

Tech stack

Artificial Intelligence Application Release Automation Cloud Engineering DevOps Fraud Prevention and Detection Machine Learning Azure Machine Learning AI Infrastructure Google Cloud Kubernetes Information Technology Machine Learning Operations
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Requirements

  • 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.

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on www.dice.com
Prepare application

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

Moments

Explore playlists

2:17 min

Mapping the maturity roadmap for scaled devops adoption

Dominik Krichbaum Dominik Krichbaum · World Congress 2026 Europe

2:28 min

Understanding Kubernetes architecture and core cluster components

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Unlocking free learning credits via Google Cloud Innovators

Asrar Asrar · World Congress 2024

5:28 min

Defining MLOps and its role in production systems

Hauke Brammer · World Congress 2023

3:18 min

Scaling global network engineering through DevOps culture

Stuart Clark · LIVE

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Centralizing LLMOps workflows within Azure AI Foundry

Maxim Salnikov Maxim Salnikov · LIVE

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From Traction to Production: Maturing your LLMOps step by step

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World Congress 2026 Europe • about 2 months ago

From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform

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Understanding Kubernetes in a visual way thumbnail
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World Congress 2026 Europe • about 2 months ago

Understanding Kubernetes in a visual way

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The Cloud is Calling: Answer with In-Demand Skills thumbnail
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World Congress 2024 • about 2 years ago

The Cloud is Calling: Answer with In-Demand Skills

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DevOps Maturity Check – a way to balance autonomy and alignment thumbnail
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DevOps Maturity Check – a way to balance autonomy and alignment

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Instant KAI Sandboxes with vCluster: Multi-Tenant, Multi-Scheduler GPU Sharing

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