MLOps Engineer: Cloud Deployment & Pipelines

CliftonLarsonAllen; Kansas City (MO); On-site; USD 110,000 - 140,000; Full time

Benefits offered by this job

Flexible PTO

Paid parental leave

Mental health coverage

Wellness stipend

Fertility benefits

Job summary

CLA, a top 10 national professional services firm, is hiring a Machine Learning Operations Engineer to join our Data Science Team in a collaborative environment. You will help design MLOps infrastructure for deployment, monitoring, and management of ML models, and collaborate with data scientists and engineers to integrate data science and engineering workflows.

You will also maintain automated pipelines, troubleshoot performance issues, and ensure security and scalability while keeping

Qualifications

• 2+ years of experience as a Data/ML Engineer.
• Combination of relevant experience, education, and training may be accepted in lieu of degree.

Responsibilities

• Assist design and development of MLOps infrastructure for deployment, management, and monitoring of ML models.
• Collaborate with data scientists and engineers to integrate data science and engineering processes.
• Maintain automated workflows and pipelines for model deployment and monitoring.
• Troubleshoot issues with model performance, data pipelines, or infrastructure.
• Help ensure security and scalability of MLOps infrastructure for large data volumes and models.
• Maintain documentation for MLOps processes and systems.
• Collaborate with cross-functional teams to translate business needs into technical requirements.
• Ensure compliance with data security and privacy regulations.
• Stay updated on industry trends in ML and MLOps.

Skills

Cloud platforms

SQL

CI/CD

Python

Data pipeline

ML model deployment

Performance monitoring

DevOps

Education

Bachelor's degree

Tools

Jenkins

Git

Perforce

Job description

CLA, a top 10 national professional services firm, is hiring a Machine Learning Operations Engineer to join our Data Science Team in a collaborative environment. You will help design MLOps infrastructure for deployment, monitoring, and management of ML models, and collaborate with data scientists and engineers to integrate data science and engineering workflows.

You will also maintain automated pipelines, troubleshoot performance issues, and ensure security and scalability while keeping