MLOPS Architect

Programmers.io; Detroit (MI); On-site; USD 150,000 - 190,000; Full time

Job summary

Programmers.io seeks an experienced MLOps Architect to design scalable ML platforms and production-grade ML/AI solutions. You will lead end-to-end ML model lifecycle, CI/CD pipelines, and cloud-native architectures using AWS/Azure/GCP.

Extensive collaboration with cross-functional teams and a focus on security, governance, and reliability are essential. The role requires hands-on experience with Docker, Kubernetes, MLflow, Kubeflow, and modern ML frameworks, plus strong cloud knowledge across

Qualifications

• 8+ years in software/cloud/ML engineering with strong MLOps experience.
• Hands-on with MLOps architecture and ML lifecycle management.
• Experience with Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Strong Docker, Kubernetes, and CI/CD experience.
• Experience with MLflow, Kubeflow, SageMaker, Azure ML, or Vertex AI.
• Knowledge of AWS, Azure, or GCP.

Responsibilities

• Design and architect scalable MLOps platforms and ML infrastructure.
• Build end-to-end ML model lifecycle from development to monitoring.
• Develop CI/CD/CT pipelines for ML models and data workflows.
• Implement model versioning, experiment tracking, and automated deployment.
• Create ML solutions using AWS, Azure, or GCP cloud platforms.
• Work with Docker and Kubernetes for containerized ML workloads.
• Implement model monitoring, drift detection, and production observability.
• Integrate data pipelines with ML training and inference workflows.
• Establish security, governance, scalability, and reliability standards.
• Collaborate with Data Scientists, ML Engineers, Data Engineers, DevOps, and Architecture teams.
• Troubleshoot production ML systems and optimize deployment processes.

Skills

MLOps

ML lifecycle

Python

Docker

Kubernetes

CI/CD

Cloud platforms

Git/Jenkins

Terraform

Model monitoring

Tools

TensorFlow

PyTorch

Scikit-learn

MLflow

Kubeflow

SageMaker

Azure ML

Vertex AI

Job description

We are looking for an experienced MLOps Architect to design and implement scalable machine learning platforms and production-grade ML/AI solutions. The ideal candidate will have strong experience across MLOps, cloud platforms, ML lifecycle management, CI/CD, automation, and Kubernetes.

Key Responsibilities

• Design and architect scalable MLOps platforms and ML/AI infrastructure.
• Build and manage end-to-end machine learning model lifecycle from development through deployment and monitoring.
• Develop CI/CD/CT pipelines for ML models and data workflows.
• Implement model versioning, experiment tracking, model registry, and automated deployment processes.
• Design ML solutions using AWS, Azure, or GCP cloud platforms.
• Work with Docker and Kubernetes for containerized ML workloads.
• Implement model monitoring, performance tracking, drift detection, and production observability.
• Integrate data pipelines with ML training and inference workflows.
• Establish security, governance, scalability, and reliability standards for ML platforms.
• Collaborate with Data Scientists, ML Engineers, Data Engineers, DevOps, and Architecture teams.
• Troubleshoot production ML systems and optimize infrastructure and deployment processes.

Required Skills

• 8+ years of experience in software/cloud/ML engineering, with strong MLOps experience.
• Strong hands-on experience with MLOps architecture and ML lifecycle management.
• Experience with Python and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Strong experience with Docker, Kubernetes, and CI/CD.
• Experience with MLflow, Kubeflow, SageMaker, Azure ML, or Vertex AI.
• Strong knowledge of AWS, Azure, or GCP.
• Experience with Git, Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
• Knowledge of model monitoring, model governance, data/model versioning, and automated deployment.
• Strong understanding of APIs, microservices, cloud architecture, and infrastructure automation.
• Experience with Terraform or similar Infrastructure-as-Code tools is preferred.

Preferred

• Experience with Generative AI/LLM deployment and MLOps.
• Experience with RAG, model serving, vector databases, or AI platforms.
• Knowledge of cloud security and enterprise governance.
• Strong communication and stakeholder-management skills.