Key skills
AzureDockerKubernetesAIMachine LearningMLGenAILLMRAGMLOpsMLflowKubeflowSageMakerVertex AICI/CD
About this role
Role Overview
- Design enterprise MLOps and LLMOps architecture
- Build deployment strategies for AI and LLM solutions
- Define model lifecycle management processes
- Implement monitoring, observability, and evaluation frameworks
- Design CI/CD pipelines for machine learning workloads
- Collaborate with AI researchers, platform engineers, and DevOps teams
- Support AI governance and security requirements
- Advise client stakeholders on operational AI best practices
Requirements
- 7+ years of experience in Machine Learning Engineering or MLOps
- 3+ years designing production-grade MLOps platforms
- Experience with LLM deployment and operationalization
- Strong knowledge of MLflow, Kubeflow, Vertex AI, Azure ML, SageMaker or similar platforms
- Experience deploying GenAI applications in enterprise environments
- Knowledge of RAG architectures, vector databases, model evaluation, and prompt management
- Experience with Kubernetes, Docker, CI/CD pipelines
- Familiarity with GPU infrastructure
- Strong understanding of AI governance and model lifecycle management
- Upper-Intermediate or higher level of English
Tech Stack
- Azure
- Docker
- Kubernetes