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Global banking brand and metro location but MLOps niche reduces applicant pool.
MLOps, Kubernetes, and cloud skills are broadly transferable across industries.
Explicit 2-7 years plus mandatory MLOps, Kubernetes, Azure, Python and CI/CD requirements.
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Own end-to-end delivery of AIops solutions focusing on AI/ML workflow automation and optimization within defined quality, timeline, and budget constraints.
Design, implement, and maintain CI/CD pipelines and infrastructure for AI/ML models, ensuring operational health and performance of AI/ML systems.
Collaborate with data scientists and engineers to integrate AI/ML models into production environments and troubleshoot any workflow or infrastructure issues.
Bachelor's or Master's degree in Computer Science, Software Engineering, or related field.
2-7 years of relevant experience in AIops, MLOps, or related domains.
Strong proficiency in Python, FastAPI, Kubernetes (or AKS), and Microsoft Azure AI/ML services including Azure ML Flow.
Experience with CI/CD tools (Jenkins, GitHub Actions, Azure DevOps), containerization (Docker), and orchestration (Kubernetes).
Experienced in implementing and managing AIops/MLops solutions in Azure cloud environments, specifically working hands-on with Azure ML Flow and Azure Kubernetes Service.
Capable of independently owning delivery with accountability on quality, timeline, and budget, indicating a senior operational and execution focus.
Familiar with integrating AI/ML models in production and maintaining operational pipelines, reflecting a strong technical and problem-solving orientation in fast-paced settings.