Experteer Overview
¿Listo para inscribirse? Antes de hacerlo, asegúrese de leer todos los detalles pertenecientes a este trabajo en la descripción a continuación.
As a Senior Platform Engineer, you design scalable backend platforms that expose AI and automation as production-grade services. You will build API-driven systems on Kubernetes, blending AI components with deterministic logic to support transformation workflows across business units. Working in a regulated financial services setting, you’ll embed compliance, governance, and responsible AI into design. You’ll partner with cross-functional teams to deliver robust APIs, observability, and scalable deployment patterns that enable enterprise AI at scale.
Compensaciones / Beneficios
• Design and maintain RESTful APIs with versioning and stable model behavior across releases
• Define validated response schemas for auditable, enterprise-ready outputs
• Implement streaming patterns (SSE/chunked) for real-time model outputs
• Implement authentication/authorization frameworks (OAuth2, JWT, RBAC) for cloud integrations
• Enforce API-layer data controls (input sanitisation, output filtering) and guardrails for compliance
• Incorporate audit logging into API contracts for regulatory traceability
• Design background jobs to return immediately while model inference runs asynchronously
• Integrate with message queues and event-driven architectures (Azure Service Bus, SQS, Kafka)
• Implement polling/webhook mechanisms to deliver results non-blockingly
• Establish structured logging with inputs/outputs/latency/model version
• Integrate with monitoring platforms (Datadog, Azure Monitor, CloudWatch) for full-stack visibility
• Implement drift detection for model outputs and versioned endpoints for seamless updates
• Develop automated testing strategies for non-deterministic outputs (contract tests, regression benchmarks)
• Deploy containerised APIs on Kubernetes (AKS/EKS) with health checks and autoscaling
• Wrap managed AI services (Databricks Model Serving, Azure OpenAI, AWS Bedrock) behind internal contracts
Responsabilidades
• 7+ years in backend engineering with Python
• Proven track record delivering AI/ML, GenAI, and agentic systems into production
• Hands-on experience with Azure/AWS xqbhyrx cloud AI services and Databricks (MLflow, Delta Lake, Model Serving)
• Strong API design and distributed systems experience (asynchronous patterns, background processing, messaging)
• Proven Kubernetes deployment experience; Terraform for infra; emphasis on observability and monitoring
• Experience in regulated environments with auditability, data controls, security, and compliance
• Ownership mindset; cross-functional collaboration; ability to build reusable platform patterns
• English language proficiency; additional European languages a plus
Requisitos principales
• hybrid work model
• bonus scheme
• pension
• employee shares program
• lifelong learning
• health and wellbeing
or continue with email
¿Listo para inscribirse? Antes de hacerlo, asegúrese de leer todos los detalles pertenecientes a este trabajo en la descripción a continuación.
As a Senior Platform Engineer, you design scalable backend platforms that expose AI and automation as production-grade services. You will build API-driven systems on Kubernetes, blending AI components with deterministic logic to support transformation workflows across business units. Working in a regulated financial services setting, you’ll embed compliance, governance, and responsible AI into design. You’ll partner with cross-functional teams to deliver robust APIs, observability, and scalable deployment patterns that enable enterprise AI at scale.
Compensaciones / Beneficios
• Design and maintain RESTful APIs with versioning and stable model behavior across releases
• Define validated response schemas for auditable, enterprise-ready outputs
• Implement streaming patterns (SSE/chunked) for real-time model outputs
• Implement authentication/authorization frameworks (OAuth2, JWT, RBAC) for cloud integrations
• Enforce API-layer data controls (input sanitisation, output filtering) and guardrails for compliance
• Incorporate audit logging into API contracts for regulatory traceability
• Design background jobs to return immediately while model inference runs asynchronously
• Integrate with message queues and event-driven architectures (Azure Service Bus, SQS, Kafka)
• Implement polling/webhook mechanisms to deliver results non-blockingly
• Establish structured logging with inputs/outputs/latency/model version
• Integrate with monitoring platforms (Datadog, Azure Monitor, CloudWatch) for full-stack visibility
• Implement drift detection for model outputs and versioned endpoints for seamless updates
• Develop automated testing strategies for non-deterministic outputs (contract tests, regression benchmarks)
• Deploy containerised APIs on Kubernetes (AKS/EKS) with health checks and autoscaling
• Wrap managed AI services (Databricks Model Serving, Azure OpenAI, AWS Bedrock) behind internal contracts
Responsabilidades
• 7+ years in backend engineering with Python
• Proven track record delivering AI/ML, GenAI, and agentic systems into production
• Hands-on experience with Azure/AWS xqbhyrx cloud AI services and Databricks (MLflow, Delta Lake, Model Serving)
• Strong API design and distributed systems experience (asynchronous patterns, background processing, messaging)
• Proven Kubernetes deployment experience; Terraform for infra; emphasis on observability and monitoring
• Experience in regulated environments with auditability, data controls, security, and compliance
• Ownership mindset; cross-functional collaboration; ability to build reusable platform patterns
• English language proficiency; additional European languages a plus
Requisitos principales
• hybrid work model
• bonus scheme
• pension
• employee shares program
• lifelong learning
• health and wellbeing
Sign in
To continue your applicationor continue with email
By continuing you agree to our Terms & Privacy Policy.
Similar jobs
Show more
