MLOPS Architect

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
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• Design and architect scalable MLOps platforms and ML/AI infrastructure
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• 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, Dev Ops, 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
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• Experience with Python and ML frameworks such as Tensor Flow, PyTorch, or Scikit-learn.
• Strong experience with Docker, Kubernetes, and CI/CD
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• Experience with MLflow, Kubeflow, Sage Maker, Azure ML, or Vertex AI
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• Strong knowledge of AWS, Azure, or GCP
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• Experience with Git, Jenkins, Git Hub Actions, Git Lab CI, or Azure Dev Ops.
• 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.

• 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.