Senior Project Engineer - Embedded Architect Kforce Inc.
Rochester Hills, MI2 days ago
Highlights
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 .
Numbers & Facts
| Location | Detroit, MI |
Description
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.
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