Senior AI DevOps / MLOps Engineer

We are looking for a Senior AI DevOps / MLOps Engineer to support the delivery of AI, ML, and GenAI solutions in a large banking environment. The role is focused on building secure, scalable, and compliant deployment pipelines for AI applications on the Microsoft Azure stack.

This project focuses on enabling enterprise grade AI and GenAI capabilities across banking systems by implementing robust MLOps practices and cloud native infrastructure. The goal is to standardize how AI models are deployed, monitored, and governed in a secure and compliant way.

• Design and maintain CI/CD and MLOps pipelines for AI/ML solutions.
• Deploy and operate AI workloads on Azure Machine Learning, Azure OpenAI, AKS, Azure Functions, and Azure DevOps.
• Automate infrastructure using Terraform, Bicep, or ARM templates.
• Implement secure deployment practices: RBAC, Key Vault, managed identities, private endpoints, network isolation.
• Set up monitoring, logging, alerting, and operational support for AI services and model endpoints.
• Support model versioning, release management, rollback, and production governance.
• Work closely with data scientists, AI engineers, cloud architects, security, and banking IT teams.
• Ensure solutions meet banking standards for security, auditability, resilience, and compliance.

• 7+ years of DevOps, Cloud Engineering, Platform Engineering, or related experience.
• Strong experience with Azure DevOps, CI/CD pipelines, Git, YAML, and release management.
• Experience with Azure Machine Learning and/or MLOps platforms.
• Practical knowledge of Azure OpenAI, GenAI applications, or AI service deployment.
• Experience with Docker, Kubernetes, and AKS.
• Experience with Infrastructure as Code: Terraform, Bicep, or ARM.
• Good understanding of monitoring tools such as Azure Monitor, Application Insights, Log Analytics, Grafana.
• Scripting skills in PowerShell, Python, or Bash.
• Experience working in regulated enterprise environments, preferably banking, fintech, or insurance.

• Experience with RAG pipelines, prompt orchestration, MLflow, or Prompt Flow.
• Knowledge of Microsoft data platforms: Databricks, Synapse, Data Factory, Microsoft Fabric, Purview.
• Understanding of AI governance, model risk, data privacy, and audit requirements.
• Experience with Microsoft security tools such as Defender for Cloud or Sentinel.