Senior AI DevOps / MLOps Engineer
Actively Hiring
Full-time Posted 14 days ago
Role overview
Position overview: 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.
Responsibilities
- check_circle Design and maintain CI/CD and MLOps pipelines for AI/ML solutions.
- check_circle Deploy and operate AI workloads on Azure Machine Learning, Azure OpenAI, AKS, Azure Functions, and Azure DevOps.
- check_circle Automate infrastructure using Terraform, Bicep, or ARM templates.
- check_circle Implement secure deployment practices: RBAC, Key Vault, managed identities, private endpoints, network isolation.
- check_circle Set up monitoring, logging, alerting, and operational support for AI services and model endpoints.
- check_circle Support model versioning, release management, rollback, and production governance.
- check_circle Work closely with data scientists, AI engineers, cloud architects, security, and banking IT teams.
- check_circle Ensure solutions meet banking standards for security, auditability, resilience, and compliance.
Basic qualifications
- 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.
Preferred qualifications
- Nice to have: 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.
About the company
Project overview: 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.
Tags & Focus Areas
Ai Machine Learning Mlops
About DataArt
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