MLOps Engineer: AI Production Pipelines & CI/CD

Placements24; Riverton (UT); Hybrid; USD 90,000 - 150,000; Full time

Benefits offered by this job

Competitive salary

Performance-based bonuses

Health and retirement benefits

Professional development opportunities

Hybrid work environment

Job summary

Placements24 is seeking an experienced MLOps Engineer to join the AI and Emerging Technologies team. You will bridge ML development and production deployment, building scalable infrastructure for training, deployment, monitoring, and retraining of models.

You will collaborate with data scientists and software engineers, implement CI/CD for ML workflows, and ensure security, reliability, and cost efficiency in production environments. Hybrid work model offered.

Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.
• 4+ years experience in software engineering or DevOps with focus on MLOps.
• Proficient in Python and ML frameworks such as TensorFlow or PyTorch.
• Hands-on experience with cloud platforms (AWS/Azure/GCP) and Docker/Kubernetes.
• Experience with CI/CD and infrastructure as code; strong problem-solving and collaboration; hybrid work.

Responsibilities

• Design, implement, and manage robust MLOps pipelines for automated model deployment, testing, and monitoring.
• Develop and maintain infrastructure for scalable ML model training and inference.
• Implement CI/CD practices tailored for ML workflows.
• Monitor model performance in production and plan retraining/updates.
• Collaborate with data scientists and software engineers to streamline the ML lifecycle.
• Ensure reliability, security, and cost-effectiveness of ML systems.

Skills

Python programming

CI/CD

Infrastructure as code

Collaboration

Problem solving

Education

Bachelor's or Master's degree in CS/Engineering

Tools

TensorFlow

PyTorch

Docker

Kubernetes

AWS

Azure

GCP

Job description

Placements24 is seeking an experienced MLOps Engineer to join the AI and Emerging Technologies team. You will bridge ML development and production deployment, building scalable infrastructure for training, deployment, monitoring, and retraining of models.

You will collaborate with data scientists and software engineers, implement CI/CD for ML workflows, and ensure security, reliability, and cost efficiency in production environments. Hybrid work model offered.