130 years of trust, time to shape what's next. Allianz Group is one of the world's leading insurers and asset managers - built on over a century of stability, expertise, and financial strength. Allianz Services is where that legacy meets the future. 8,200+ people. Eight countries. Three continents. We deliver specialized services to clients within Allianz Group, powered by AI, advanced analytics, and a mindset that refuses to stand still. From core insurance operations to engineering and consulting services, we reimagine how insurance works. What truly sets us apart are our people and the trusted partnerships we build, anchored in our values of expertise, integrity, and empowerment. We don't support the business. We shape it.

Role Overview:

We are looking for a Senior Solutions Engineer (m/f/d) to join the Data and AI Transformation Engineering team, responsible for designing and building scalable backend platforms that expose AI and automation capabilities as reliable, production grade services.
The role focuses on developing API driven systems deployed on Kubernetes, combining AI driven components with deterministic logic to support complex transformation and automation use cases across multiple business units. Operating in a regulated financial services environment, the role requires a strong engineering mindset with compliance, governance, and responsible use of AI treated as core design principles.

What you do:

Design and maintain RESTful APIs with robust versioning strategies, ensuring stability as model behaviour evolves across releases.
Define structured, validated response schemas that deliver deterministic, auditable outputs suitable for enterprise consumption.
Implement streaming response patterns (SSE / chunked HTTP) to enable real-time model output delivery to frontends and downstream systems.
Implement authentication and authorisation frameworks (OAuth 2.0, JWT, RBAC, scopes) including service principal patterns for cloud-to-cloud integration across Azure, AWS, and Databricks.
Enforce data controls at the API layer — including input sanitisation, output filtering, and guardrails — ensuring responses meet compliance standards before leaving the system.
Build audit logging into the API contract so that every request and response is fully traceable for regulatory purposes.
Design background job patterns that return immediately to callers while model inference executes asynchronously.
Integrate with message queues and event-driven architectures (Azure Service Bus, SQS, Kafka) for pipeline orchestration.
Implement polling and webhook callback mechanisms to deliver results without blocking consumers.
Establish structured logging on every API call — capturing inputs, outputs, latency, and model version metadata.
Integrate with enterprise monitoring platforms (Datadog, Azure Monitor, CloudWatch) to ensure full-stack visibility.
Implement drift detection hooks that flag when model outputs deviate from expected patterns.
Manage versioned model endpoints, enabling seamless deployment of new model versions without breaking existing API consumers.
Develop automated testing strategies for non-deterministic outputs, including contract tests, output validation, and regression benchmarks.
Deploy containerised APIs on Kubernetes (AKS/EKS) with health checks, autoscaling, and production-grade reliability.
Wrap managed AI services (Databricks Model Serving, Azure OpenAI, AWS Bedrock) behind clean, internal API contracts.

What you bring:

University degree in Data Science, Data Engineering, Computer Science, Mathematics, Statistics, or a related field (or equivalent experience).
7+ years of experience in backend engineering with Python, with a proven track record of delivering AI/ML, GenAI, and agentic systems into enterprise production environments (beyond prototypes or proofs of concept).
Hands on experience with cloud based AI services across Azure and AWS, including strong expertise in Databricks (MLflow, Delta Lake, Model Serving).
Strong background in designing clean, versioned APIs and building distributed systems, including asynchronous patterns, background processing, and message based architectures.
Proven experience deploying and operating services on Kubernetes, managing infrastructure using Terraform, and treating observability, structured logging, and monitoring as standard engineering practices.
Experience working in regulated environments, where auditability, data controls, security, and compliance are core engineering requirements.
Demonstrated ownership mindset: able to take systems from design through production and incident resolution, collaborate across business units, and create reusable platform patterns that enable other teams.
Good level of English (required), other languages (e.g. Spanish or additional European languages) are nice to have.
We highly welcome candidates with a genuine interest and affinity for Information Technology (IT) and Generative Artificial Intelligence (GenAI), as these attributes are considered valuable assets to our team.

What we offer:

We offer a hybrid work model which recognizes the value of striking a balance between in-person collaboration and remote working incl. up to 25 days per year working from abroad.
We believe in rewarding performance and our compensation and benefits package includes a company bonus scheme, pension, employee shares program and multiple employee discounts (details vary by location).
From career development and digital learning programs to international career mobility, we offer lifelong learning for our employees worldwide and an environment where innovation, delivery and empowerment are fostered .
Flexible working, health and wellbeing offers (including healthcare and parental leave benefits) support to balance family and career and help our people return from career breaks with experience that nothing else can teach.

1 Job for Structured Logging in Spain

Senior Solutions Engineer (m/f/d)