Job Description
- Define and implement the ML/AIOps strategy, architecture, and operational framework
- Design scalable solutions across classical ML, Generative AI, and Agentic AI domains
- Conduct gap analyses of current ML/AI processes and recommend strategic improvements
- Build and maintain CI/CD pipelines for ML models, including automation and monitoring
- Establish model governance, observability, and lifecycle management practices
- Create documentation, reusable components, and best practice guidelines
- Collaborate with cross-functional teams
- Participate in occasional business travel to support stakeholder engagement
- Hands-on experience with MLOps in production-grade environments
- Expertise in ML orchestration tools (e.g., Kubeflow, MLflow, Vertex AI, SageMaker)
- Strong grasp of model deployment,...
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