Backend Engineer within Advanced Analytics

Key Responsibilities As a Backend Engineer within Advanced Analytics (DA3) in the Chief Data & AI Office area at Allianz Partners, you will join our central AI team to build reusable AI foundations and production-grade AI services on a global scale. We are looking for an engineer with strong software engineering fundamentals and hands-on cloud and DevOps expertise to design, ship, and operate cloud-native systems that power AI-enabled solutions across the organization. You will work in a cross-functional environment with ML Engineers, Platform Engineers, AI Architects, and DevOps, taking end-to-end ownership from design through reliable operations. In this role, you will help establish consistent engineering standards for security, observability, and delivery, and you will contribute to scalable internal platforms and shared services that accelerate teams worldwide. As a Backend Engineer, you are in charge of the following responsibilities: - Design, build, and maintain backend

services and REST APIs powering AI-enabled solutions: clean interfaces, robust domain models, and maintainable data access patterns. - Deliver cloud-native microservices using Docker and Kubernetes, with attention to scalability, resilience, and cost. - Build and maintain CI/CD pipelines with automated tests, quality gates, and secure delivery practices. - Integrate and standardize API exposure via gateway patterns, including usage policies, throttling, authentication/authorization, and versioning (Azure API Management where applicable). - Establish and maintain observability standards using Azure Monitor and Application Insights (dashboards, alerting, distributed tracing, and runbooks). - Diagnose and resolve complex issues across microservices, clusters, and pipelines; perform root-cause analysis and preventative improvements. - Embed security and resilience: secrets management, least privilege, container security best practices, and disaster recovery patterns. - Engineer for regulated environments: implement audit-friendly practices such as traceable changes, reliable logging, and disciplined handling of sensitive data (minimization, access controls, retention). - Collaborate with ML Engineers, Platform Engineers, AI Architects, and DevOps to deliver shared foundations and consistent standards across global teams. What You Bring - 5+ years professional backend engineering experience; MLOps or AI platform experience is a strong plus. - Expert-level Python; ideally FastAPI, Pydantic, SQLAlchemy (or equivalent frameworks). - Strong engineering fundamentals: testing strategies, REST API versioning, documentation, code quality, and pragmatic system design. - Production experience with Docker and Kubernetes. - Strong CI/CD experience (GitHub Actions preferred; ArgoCD or similar also relevant). - Familiarity with Azure cloud platforms and services, particularly AKS, ACR, Key Vault, Azure Monitor/Application Insights, and Azure API Management (APIM). - Strong experience with PostgreSQL including schema design, migrations, and performance tuning fundamentals. - Familiarity with distributed systems and event-driven architectures using Kafka. - Strong troubleshooting skills and an operational mindset. Ways of Working - Comfortable in agile, iterative delivery environments with ownership and accountability. - Clear communicator and collaborator across global, cross-functional stakeholders. - Proactive learner with pragmatic adoption of AI-assisted developer tools (for example GitHub Copilot, Claude Code) to improve developer experience and delivery. Nice to Have - MLOps exposure: model packaging/deployment patterns, batch vs real-time inference, feature pipelines, experiment tracking. - Experience building internal libraries, platforms, or shared tooling used by multiple teams. - Experience in regulated environments where auditability and secure-by-default delivery are essential. What We Offer We offer training and development