MLOps Engineer – Remote Contractor (Full-Time)

Ardan Labs; United States; Remote; USD 90,000 - 150,000; Full time

Job summary

An innovative firm is seeking a skilled MLOps Engineer to support the deployment and scaling of machine learning systems. This role involves designing and managing scalable ML workloads on Kubernetes, developing CI/CD pipelines, and utilizing Infrastructure as Code practices. The ideal candidate will bridge the gap between data science and production-grade ML infrastructure, collaborating with high-performing teams to implement best practices and ensure system reliability. Join a company that values clean architecture and high engineering standards, and be part of cutting-edge projects in the software development industry.

Benefits

Collaborative work environment

Access to cutting-edge technologies

Opportunities for professional growth

High engineering standards

Clean architecture practices

Qualifications

• 5+ years in software engineering, DevOps, or data infrastructure.
• 3+ years in MLOps or production ML systems.
• Strong knowledge of Docker and Kubernetes.

Responsibilities

• Design and manage scalable ML workloads on Kubernetes.
• Develop GitLab CI/CD pipelines for ML models.
• Utilize Terraform for cloud infrastructure management.

Skills

Software Engineering

DevOps

MLOps

Python

Go

Docker

Kubernetes

CI/CD

Cloud Platforms (AWS/GCP/Azure)

Communication Skills

Education

Bachelor's Degree in Computer Science or related field

Tools

GitLab CI/CD

Terraform

Helm

Kustomize

Argo CD

Airflow

Kubeflow

MLflow

SageMaker

Vertex AI

Job description

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Ardan Labs is seeking a skilled MLOps Engineer to support the deployment, automation, and scaling of machine learning systems for one of our fast-growing clients. The ideal candidate has a strong engineering foundation, deep DevOps knowledge, and hands-on experience bridging the gap between data science and production-grade ML infrastructure.

Responsibilities

1. Kubernetes & ML Workload Deployment: Design, deploy, and manage scalable ML workloads, including LLMs, on Kubernetes clusters. Implement best practices for container orchestration, ensuring high availability and fault tolerance.
2. CI/CD Pipeline Development: Develop and maintain GitLab CI/CD pipelines for automated testing, integration, and deployment of APIs and ML models. Implement advanced release strategies, such as blue-green deployments and canary releases, to minimize downtime and ensure reliability.
3. Infrastructure as Code (IaC): Utilize Terraform to provision and manage cloud infrastructure on Google Cloud Platform (GCP). Ensure infrastructure is modular, reusable, and adheres to best practices for scalability and security.
4. Configuration Management: Employ Helm and Kustomize for managing Kubernetes manifests and application configurations. Streamline deployment processes and maintain consistency across environments.
5. Continuous Deployment & Monitoring: Integrate Argo CD for continuous deployment, ensuring that applications are always in sync with the desired state. Monitor system performance, implement logging, and set up alerting mechanisms to proactively address issues.
6. Collaboration & Documentation: Work closely with data scientists, software engineers, and DevOps teams to understand requirements and deliver solutions. Document processes, architectures, and workflows to facilitate knowledge sharing and onboarding.

Requirements

1. 5+ years of professional experience in software engineering, DevOps, or data infrastructure
2. 3+ years of hands-on experience with MLOps, ML pipeline automation, or production ML systems
3. Strong knowledge of Docker and Kubernetes (preferably with real-time ML use cases)
4. Experience working with CI/CD tools for machine learning workflows
5. Proficiency in Python and/or Go (experience writing CLI tools or ML-related services a big plus)
6. Familiarity with model training and orchestration tools such as Airflow, Kubeflow, MLflow, SageMaker, or Vertex AI
7. Experience with cloud platforms (AWS/GCP/Azure), especially for model deployment and scaling
8. Excellent communication skills and ability to collaborate cross-functionally with data and engineering teams

Nice to Have

1. Experience with feature stores, data validation frameworks, or data versioning tools like DVC
2. Exposure to streaming data systems (Kafka, Pulsar) for online inference or feature pipelines
3. Familiarity with security practices for ML systems and data compliance (GDPR, HIPAA)
4. Contributions to open-source MLOps tools or platforms

Why Work With Ardan Labs

• Collaborate with high-performing teams on cutting-edge systems and technologies
• Join a team that values clean architecture, automation, and high engineering standards

Seniority level

• Mid-Senior level

Employment type

• Full-time

Job function

• Engineering and Information Technology

Industries

• Software Development

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