About the Role

We are seeking a Cloud DevSecOps Engineer (AI Products) to join our growing AI Product Engineering team. This role is ideal for someone passionate about cloud security, DevOps automation, and secure AI product delivery. You will be responsible for designing, securing, and automating cloud infrastructure that powers our Retrieval-Augmented Generation (RAG) platform, ensuring that our deployments are scalable, resilient, and enterprise-ready.

As part of a cross-functional team, you will collaborate with product managers, data scientists, and software engineers to ensure our AI products meet the highest standards of security, compliance, and performance.

Key Responsibilities

Cloud Architecture & Infrastructure

• Design and maintain secure, scalable Azure cloud environments for AI product deployments.
• Build and manage hub-and-spoke network topologies with appropriate segmentation, firewalls, and private endpoints.

DevSecOps & Automation

• Develop and maintain Terraform scripts for Infrastructure-as-Code (IaC) to ensure consistent and repeatable deployments.
• Implement CI/CD pipelines (Azure DevOps) to automate testing, integration, and deployment of RAG workloads.
• Embed security practices into DevOps pipelines (DevSecOps).

Cloud Security & Compliance

• Enforce robust identity and access management (IAM) and role-based access controls (RBAC).
• Configure managed identities, Key Vault, and credential-free operations to reduce risks.
• Strengthen security posture through encryption, TLS enforcement, firewall rules, and least-privilege access.

AI Product Enablement

• Deploy and monitor containerized RAG workloads (Azure Container Apps, Kubernetes).
• Support product teams in running secure and efficient large language model (LLM) workloads.
• Contribute to best practices for secure and scalable AI infrastructure.

Requirements:

• Master’s degree in Computer Science, Cybersecurity, or related field.
• 2–4 years of relevant professional experience in cloud security, DevOps, or infrastructure engineering.
• Strong hands-on experience with Azure cloud services, Terraform, and CI/CD pipelines.
• Knowledge of containerization (Docker, Kubernetes) and cloud networking.
• Understanding of security best practices, IAM, encryption, and compliance frameworks.

Preferred:

• Experience with AI/ML product infrastructure (RAG, Azure OpenAI, Databricks, etc.).
• Exposure to multi-cloud environments (AWS or GCP).
• Familiarity with MLOps practices and monitoring AI workloads.

What We Offer

• Opportunity to work on cutting-edge AI products that shape the future of infrastructure and urban planning.
• Exposure to end-to-end product delivery, from architecture to deployment.
• A collaborative environment where security, AI, and engineering meet.
• Professional growth with mentorship and career progression in cloud security, DevSecOps, and AI infrastructure.