Machine Learning Operations (MLOps) Engineer

Placements24; United States; Hybrid; USD 94,000 - 148,000; Full time

Benefits offered by this job

Competitive salary

Hybrid work environment

Health and retirement benefits

Professional development and certs

Work on AI infrastructure

Job summary

Placements24 is seeking a skilled MLOps Engineer to join our client’s team in London. You will bridge ML model development and production deployment, building and maintaining scalable infrastructure and processes for reliable deployment, monitoring, and updates.

In a hybrid role, you will collaborate with data scientists and software engineers to streamline the ML lifecycle, implement CI/CD pipelines, containerization, and experiment tracking, and ensure robustness and reproducibility across

Qualifications

• Bachelor's degree in Computer Science, Engineering, or a related field.
• Proven experience in MLOps, DevOps, or a related role focusing on automation and infrastructure.
• Proficiency in programming languages such as Python.
• Experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
• Familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn) and ML experiment tracking tools.
• Strong understanding of software engineering principles and agile methodologies.

Responsibilities

• Design, implement, and manage CI/CD pipelines for machine learning models.
• Develop and maintain infrastructure for training, deploying, and monitoring ML models at scale.
• Implement best practices for MLOps, including version control, testing, and automation.
• Collaborate with data scientists and software engineers to ensure smooth model deployment and integration.
• Monitor model performance in production and implement strategies for retraining and updates.
• Troubleshoot and resolve issues related to ML infrastructure and model deployment.

Skills

MLOps

DevOps

CI/CD

Python

Agile

Model deployment

Education

Bachelor's degree in Computer Science, Engineering, or related field

Tools

AWS

Azure

GCP

Docker

Kubernetes

TensorFlow

PyTorch

scikit-learn

MLflow

Git

Job description

About the Role

Our client is seeking a skilled Machine Learning Operations (MLOps) Engineer to join their growing team in East London . This role is crucial for bridging the gap between machine learning model development and reliable deployment into production environments. You will be responsible for building and maintaining the infrastructure and processes that enable the efficient deployment, monitoring, and scaling of ML models. Working in a hybrid capacity, you will collaborate with data scientists and engineers to streamline the ML lifecycle, ensuring robustness and reproducibility. This is an exciting opportunity to play a key role in operationalizing cutting-edge AI solutions.

Key Responsibilities

• Design, implement, and manage CI/CD pipelines for machine learning models.
• Develop and maintain infrastructure for training, deploying, and monitoring ML models at scale.
• Implement best practices for MLOps, including version control, testing, and automation.
• Collaborate with data scientists and software engineers to ensure smooth model deployment and integration.
• Monitor model performance in production and implement strategies for retraining and updates.
• Troubleshoot and resolve issues related to ML infrastructure and model deployment.

Requirements

• Bachelor's degree in Computer Science, Engineering, or a related quantitative field.
• Proven experience in MLOps, DevOps, or a related role focusing on automation and infrastructure.
• Proficiency in programming languages such as Python.
• Experience with cloud platforms (AWS, Azure, GCP) and containerization technologies (Docker, Kubernetes).
• Familiarity with ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and ML experiment tracking tools.
• Strong understanding of software engineering principles and agile methodologies.

Benefits

• Competitive salary and performance-based bonuses.
• Hybrid work environment offering flexibility.
• Comprehensive health and retirement benefits package.
• Opportunities for professional development and certifications.
• Work on critical infrastructure for AI and machine learning projects.