Machine Learning Operations (MLOps) Engineer

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

About the Role

Our client is seeking an experienced Machine Learning Operations (MLOps) Engineer to join their AI and Emerging Technologies team in Upington . This role is vital for bridging the gap between machine learning model development and production deployment, ensuring scalability, reliability, and efficiency. You will be responsible for building and maintaining the infrastructure and processes required for deploying, monitoring, and managing ML models in production environments. This is a key position for someone passionate about automation, continuous integration/continuous deployment (CI/CD), and ensuring the smooth operation of AI systems.

Key Responsibilities

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

Requirements

• Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
• Proven experience (4+ years) in software engineering or DevOps, with a focus on MLOps.
• Strong programming skills in Python and experience with ML frameworks (e.g., TensorFlow, PyTorch).
• Hands-on experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
• Familiarity with CI/CD tools and practices, and infrastructure as code.
• Excellent problem-solving and collaboration skills, adaptable to a hybrid work model.

Benefits

• Competitive salary and performance-based bonuses.
• Comprehensive health and retirement benefits.
• Opportunities for professional development and skill enhancement.
• Hybrid work environment combining office and remote flexibility.
• Be part of a team driving innovation in AI and ML.