Remote MLOps Engineer | Scale AI/ML in Production

AgileEngine; Dallas (TX); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Growth opportunities

Competitive compensation

Remote work

Modern projects

Collaborative culture

Well-being programs

Job summary

AgileEngine is seeking a Middle/Senior MLOps Engineer to own the full lifecycle from AI/ML experimentation to scalable production deployment. You will build and maintain infrastructure, pipelines, and automation, enabling cost-effective model deployment at scale.

Work closely with data scientists to translate experiments into robust production solutions, manage cloud environments with GPU resources, and implement monitoring, drift detection, and model versioning for reproducibility.

Qualifications

• 3+ years of professional experience in MLOps, DevOps, Data Engineering, Machine Learning, or Software Engineering

Responsibilities

• Own the complete lifecycle transition from AI/ML experimentation to reliable, high-performance production deployment
• Build, maintain, and scale the infrastructure, automation, and CI/CD workflows necessary for rapid and efficient model deployment
• Implement robust production monitoring systems, build visibility dashboards, and set up data and concept drift detection to ensure ongoing model accuracy and system reliability
• Manage experiment tracking and model versioning to ensure full reproducibility and traceability of all models in production
• Partner closely with data scientists and AI researchers to translate experimental models into robust, production-ready solutions
• Manage cloud environments and GPU compute resources to ensure systems are not only highly scalable but also cost-effective

Skills

MLOps experience

Cloud environments

CI/CD pipelines

English proficiency

Education

Degree in Computer Science or Software Engineering

Tools

Docker

Kubernetes

GPU compute management

Job description

AgileEngine is seeking a Middle/Senior MLOps Engineer to own the full lifecycle from AI/ML experimentation to scalable production deployment. You will build and maintain infrastructure, pipelines, and automation, enabling cost-effective model deployment at scale.

Work closely with data scientists to translate experiments into robust production solutions, manage cloud environments with GPU resources, and implement monitoring, drift detection, and model versioning for reproducibility.