AI Engineer II - Backend

Microsoft•Redmond, WA

1d•$100,600 - $199,000

About The Position

The Security AI Platform team builds and operates production infrastructure that powers AI-native security capabilities at Microsoft scale. The Platform + Apps group develops the core product, including microservices, ML inference pipelines, APIs, and user interfaces. We work closely with the AI Operations team who handles deployments, pipelines, and production reliability. We are seeking an AI Engineer II - Backend to implement features across our backend services. In this role, you will build microservice components, API endpoints, and data pipelines within established architectures. You will work with senior engineers who will guide your technical growth while you take ownership of features within the platform.

Requirements

Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings:
Microsoft Cloud Background Check: This position will be required to pass the Microsoft background and Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice To Haves

Master's Degree in Computer Science or related technical field AND 3+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 5+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
2+ years software engineering experience building backend services
2+ years proficiency in C#/.NET or similar languages with solid API design practices
1+ years hands-on experience with ML/AI systems (coursework, projects, or production)
Experience building on Azure: AKS, Event Hub, Cosmos DB, Redis, Key Vault, Managed Identity
Exposure to ML inference technologies: model serving frameworks, inference runtimes, or ML pipelines
Distributed systems fundamentals: concurrency, messaging patterns, caching, and basic performance optimization
Experience with gRPC and Protocol Buffers in addition to REST APIs
CI/CD and containerization experience: Docker, Kubernetes basics, Azure DevOps or GitHub Actions
Observability experience: logging, metrics, tracing, and basic debugging in distributed systems
Security domain exposure: threat detection concepts, attack patterns, or security tooling
Track record shipping backend features used by other teams or customers

Responsibilities

Implement backend microservices and API endpoints using C#/.NET and Python within established architectures, deployed on Azure Kubernetes Service (AKS)
Build features across ML inference pipelines: integrate with rule engines, LLM serving platforms, and various model runtime services
Implement data pipelines and storage integrations: Event Hub connectors, Kusto queries, Redis caching layers, and configuration management
Add observability to services: OpenTelemetry instrumentation, logging, metrics collection, and health check endpoints
Optimize service performance: implement caching strategies, batching, and response streaming under guidance from senior engineers
Write comprehensive automated tests: unit tests, integration tests, and API contract tests
Participate in design discussions and code reviews; learn architectural patterns and best practices from senior engineers
Document APIs, service behaviors, and operational procedures
Deploy and debug services in development environments; coordinate with AI Operations for production deployments
Fix bugs and address issues; participate in incident triage with guidance from senior engineers
Collaborate with frontend engineers to understand API requirements and deliver endpoints they need