Backend engineer designing and operating distributed services for a global software conglomerate’s enterprise AI platform. Building APIs, orchestration, tenancy, integrations, and reliability capabilities.
Responsibilities
Design, build, and operate core services powering an enterprise AI platform
Contribute to the AI gateway, orchestration layer, entitlements, tenancy model, data access patterns, integration frameworks, APIs, and platform services
Own services end to end from design and implementation through testing, deployment, reliability, and support
Design reliable backend services, distributed systems, platform APIs, and clean service boundaries
Support product teams and customers that depend on the platform services
Requirements
Experience building and operating production backend, API, or distributed platform services
Strong software engineering skills with experience designing reliable, testable, and maintainable services
Experience with AI gateway, orchestration, entitlements, tenancy, metering, quotas, or similar platform capabilities
Ability to design APIs and developer experiences for internal and external consumers
Experience with multi-tenant systems, tenant isolation, service boundaries, and platform-level enforcement patterns
Experience building integration frameworks, connector patterns, governed data access, or reliable data on-ramps
Strong understanding of retries, partial failure, consistency, idempotency, back-pressure, and operational failure modes
Experience owning services through design, development, testing, deployment, monitoring, and support
Strong communication and collaboration skills for client-facing consulting environments
Pragmatic mindset with the ability to ship meaningful value quickly while maintaining engineering quality
Experience with C#, Python, Go, or similar backend languages
Experience with API gateways, orchestration systems, connector frameworks, event-driven systems, or platform engineering
Experience with AI, LLM, RAG, agentic workflows, tool/function calling, MCP, A2A, or AI development lifecycle practices
Experience with event-sourced metering, quotas, entitlements, or billing-related platform services
Experience building enterprise-scale software used by multiple products, customers, or internal teams
Experience using AI-assisted development tools while maintaining strong review, testing, and architecture discipline
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