MLOPS Architect

Job Description

Key Responsibilities

• Design and architect scalableMLOps platforms and ML/AI infrastructure.
• Build and manage end-to-endmachine learning model lifecycle from development through deployment and monitoring.
• DevelopCI/CD/CT pipelines for ML models and data workflows.
• Implement model versioning, experiment tracking, model registry, and automated deployment processes.
• Design ML solutions usingAWS, Azure, or GCP cloud platforms.
• Work withDocker 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 withMLOps architecture and ML lifecycle management.
• Experience withPython and ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
• Strong experience withDocker, Kubernetes, and CI/CD.
• Experience withMLflow, Kubeflow, SageMaker, Azure ML, or Vertex AI.
• Strong knowledge ofAWS, 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 withTerraform 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.