MLOps Engineer

Diverse Lynx; United States; Remote; USD 90,000 - 150,000; Full time

Job summary

An innovative firm is seeking a proactive MLOps Engineer to bridge data science and production engineering. In this role, you will build and maintain the infrastructure and workflows necessary for developing, testing, deploying, and monitoring machine learning models at scale. You will collaborate closely with data scientists to ensure models are scalable and reliable while managing and optimizing compute infrastructure. This position offers a chance to work with cutting-edge technologies in a dynamic environment, making a significant impact in the field of machine learning.

Qualifications

• 3+ years in ML Engineering or DevOps focusing on ML workflows.
• Proficient in cloud platforms and orchestration tools.

Responsibilities

• Build and maintain CI/CD pipelines for ML model development.
• Collaborate with data scientists to operationalize models.

Skills

ML Engineering

DevOps

Infrastructure Engineering

Python

CI/CD

Cloud Platforms (AWS, GCP, Azure)

MLOps Frameworks (MLflow, Kubeflow, SageMaker)

Infrastructure-as-Code (Terraform, Helm)

Monitoring Tools (Prometheus, Grafana, Datadog)

Tools

Kubernetes

Airflow

Job description

Job Title: MLOps Engineer
Location: Remote

Department: Machine Learning / Engineering
Job Type: Full-time
Overview:
We are seeking a skilled and proactive MLOps Engineer to help bridge the gap between data science and production engineering. You’ll be responsible for building and maintaining the infrastructure, tooling, and workflows required to develop, test, deploy, and monitor machine learning models at scale.
Responsibilities:

• Build and maintain CI/CD pipelines for ML model development, testing, and deployment.
• Develop reusable tools and frameworks for data processing, model training, validation, and monitoring.
• Collaborate closely with data scientists to operationalize models, ensuring they are scalable, reliable, and reproducible.
• Manage and optimize compute infrastructure, including cloud and on-prem GPU/CPU clusters.
• Implement observability and monitoring systems to track model performance, drift, and data integrity in production.
• Ensure governance and compliance through model versioning, reproducibility, and auditability.

Requirements:

• 3+ years of experience in ML Engineering , DevOps , or Infrastructure Engineering with a focus on ML workflows.
• Proficiency with cloud platforms (AWS, GCP, Azure) and orchestration tools (Kubernetes, Airflow, etc.).
• Experience with MLOps frameworks such as MLflow, Kubeflow, Metaflow, or SageMaker.
• Strong coding skills in Python and experience with infrastructure-as-code tools (e.g., Terraform, Helm).
• Solid understanding of CI/CD practices and monitoring tools (e.g., Prometheus, Grafana, Datadog).

Nice to Have:

• Experience deploying real-time inference services and batch prediction pipelines.
• Familiarity with model explainability, fairness, and responsible AI practices.
• Exposure to feature stores (e.g., Feast, Tecton) and experiment tracking platforms.

Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.