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# Senior Platform Engineer

- **Company:** Allianz Group
- **Location:** Barcelona, Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Audit Trail, Automation of Tests, Microsoft Azure, Data Control, Information Engineering, Distributed Systems, Python (Programming Language), OAuth, Queueing Systems, Role-Based Access Control, JSON Web Token, Management of Software Versions, Azure Service Bus, Datadog, Data Logging, Cloud Monitoring, Generative AI, Event Driven Architecture, Data Lakes, AI Platforms, Kubernetes, Information Technology, Apache Kafka, Machine Learning Operations, Cloud Integration, Cloudwatch, Restful APIs, Amazon Simple Queue Service (SQS), Terraform, Data Pipelines, Api Management, Databricks
- **Published:** May 17, 2026
- **Apply:** https://es.indeed.com/viewjob?jk=f3f683a1bcb076ba

## About the Role

Do you have experience in Unity?, Do you have a Bachelor's degree?, * 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 (Gen)AI, as these attributes are considered valuable assets to our team.

## Description

We are looking for a Senior Platform Engineer 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., * 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
 * Abstract cloud-specific SDKs to provide business units with a unified interface, regardless of the underlying model provider


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