About the job
We are looking for a talented and highly skilled Senior Platform Architect to spearhead the design and implementation of our robust Azure-based SaaS platform. This platform comprises Java-based microservices, a ReactJS frontend, machine learning pipelines, and retrieval-augmented generation (RAG) components.
In this pivotal role, you will collaborate closely with Java Tech Leads, Senior DevOps Engineers, Senior ML Engineers, Security teams, and business stakeholders to establish a secure, scalable, and efficient cloud-native architecture. Ensuring security is paramount in this position.
Key Responsibilities:
1. Platform Architecture & Design
- Define comprehensive architecture for the Azure-based SaaS platform.
- Design a secure, scalable microservices architecture (preferably using Java/Spring Boot).
- Establish cloud-native architecture patterns (12-factor app, event-driven, API-first).
- Create a high-availability, multi-region deployment strategy.
- Set platform standards for:
- Observability
- Logging
- Monitoring
- API gateway
- Service mesh (if required)
2. Security Architecture (Critical Area)
- Design and implement Zero Trust Architecture principles.
- Establish secure authentication & authorization mechanisms (OAuth2, OIDC, Azure AD, RBAC, ABAC).
- Lead the identity and access management strategy.
- Ensure secure API design and protection (rate limiting, throttling, WAF, API management).
- Define encryption standards (in transit & at rest).
- Implement a secrets management strategy (Azure Key Vault).
- Conduct threat modeling and security architecture reviews.
- Ensure compliance with industry standards (ISO 27001, SOC2, GDPR, etc.).
- Define DevSecOps practices and establish a secure SDLC.
- Secure ML and RAG pipelines (data protection, prompt injection mitigation, model access control).
3. Azure Cloud Architecture
- Design and oversee:
- Azure Kubernetes Service (AKS)
- Azure App Services
- Azure API Management
- Azure Storage & Databases (SQL/Cosmos/Blob)
- Event Hub / Service Bus
- Azure AI services (if applicable)
- Define landing zones and governance model.
- Implement cost optimization strategies (FinOps awareness).
- Design multi-tenant SaaS architecture.
4. ML & RAG Platform Enablement
- Architect infrastructure for:
- ML model training and deployment
- RAG pipelines (vector databases, embeddings, LLM integration)
- Define data isolation and model security controls.
- Ensure performance, scalability, and latency optimization.
- Establish monitoring for ML inference & model drift.
5. DevOps & Platform Engineering
- Work collaboratively with DevOps on:
- CI/CD pipeline architecture.
- Infrastructure as Code (IaC) practices.
