Job 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.

Requirements

Must-haves

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.
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.

Nice-to-haves

Experience with Terraform or similar Infrastructure-as-Code tools is 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.

Application prep

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Keywords to mirror

machine learningmlaici/cdkubernetesawsazuregcpdockerpythonllm

Platform: jobdiva
Effort: medium