Machine Learning Ops Engineer

myBridge Corporation; Boston (MA); Presencial; USD 120.000 - 170.000; Jornada completa

Descripción de la vacante

myBridge Corporation seeks a skilled Machine Learning Ops Engineer to design, deploy, monitor, and maintain scalable ML systems in production environments. The role combines ML expertise with software engineering, cloud infrastructure, automation, and DevOps practices to ensure secure and performant ML lifecycles.

The candidate will design end-to-end deployment pipelines, automate training and retraining workflows, and manage CI/CD for ML applications.

Formación

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field.
• 3–6 years of relevant experience in MLOps, ML engineering, DevOps, or platform engineering.
• Strong Python programming and software engineering fundamentals.
• Experience with ML lifecycle tools and cloud platforms.

Responsabilidades

• Design and maintain end-to-end ML deployment pipelines.
• Automate model training, testing, validation, deployment, and retraining workflows.
• Build and manage CI/CD pipelines for ML applications.
• Implement model versioning, experiment tracking, and artifact management.
• Collaborate with data scientists, ML engineers, software developers, and infrastructure teams.
• Deploy ML models using containers and orchestration technologies.
• Manage cloud-based ML infrastructure and optimize resources.
• Establish model monitoring, logging, alerting, and rollback procedures.
• Implement security, access controls, and governance standards for ML systems.
• Troubleshoot production incidents and improve pipeline reliability.
• Maintain technical documentation and operational best practices.

Conocimientos

Python

MLflow

Kubeflow

Docker

Kubernetes

Git

CI/CD

AWS

Azure

GCP

REST APIs

Microservices

Data pipelines

Feature engineering

Model monitoring

Educación

Bachelor's degree

Herramientas

Docker

Kubernetes

Git

CI/CD

MLflow

Kubeflow

REST APIs

Descripción del empleo

We are seeking a skilled Machine Learning Ops Engineer to develop, deploy, monitor, and maintain reliable machine learning systems in production environments. The ideal candidate will combine machine learning knowledge with software engineering, cloud infrastructure, automation, and DevOps practices to ensure ML models remain scalable, secure, and performant throughout their lifecycle.

Key Responsibilities

• Design and maintain end-to-end machine learning deployment pipelines.
• Automate model training, testing, validation, deployment, and retraining workflows.
• Build and manage CI/CD pipelines for machine learning applications.
• Implement model versioning, experiment tracking, and artifact management.
• Collaborate with data scientists, ML engineers, software developers, and infrastructure teams.
• Deploy ML models using containerization and orchestration technologies.
• Manage cloud-based ML infrastructure and optimize computing resources.
• Establish model monitoring, logging, alerting, and rollback procedures.
• Implement security, access controls, and governance standards for ML systems.
• Troubleshoot production incidents and improve pipeline reliability.
• Maintain technical documentation and operational best practices.

Required Skills

• Strong Python programming and software engineering fundamentals.
• Experience with ML lifecycle tools such as MLflow, Kubeflow, or equivalent platforms.
• Familiarity with Docker, Kubernetes, Git, and CI/CD automation.
• Experience with AWS, Microsoft Azure, or Google Cloud.
• Knowledge of model serving, REST APIs, and microservices.
• Understanding of data pipelines, feature engineering, and model monitoring.

Education and Experience

• Bachelor's degree in Computer Science, Data Science, Engineering, or a related field.
• Typically 3–6 years of relevant experience in MLOps, machine learning engineering, DevOps, or platform engineering.

Portsmouth, Rhode Island, Portsmouth, Rhode Island, United States