Build internal AI-native apps, MCP connectors, multi-agent workflows, and reusable platform components for enterprise operations across Finance, People, and GTM. Strong Python, system design, enterprise integrations, and applied AI experience required.
230k – 385k
On-siteBackend Engineering
About the role
Responsibilities
Build internal apps for enterprise operations across Finance, People, and GTM
Build MCP connectors and enterprise integrations with strong auth, permissions, idempotency, retries, and rate-limit handling
Design end-to-end multi-agent workflows with tool routing, human approvals, audit trails, and safe action boundaries
Design data architecture for operational AI systems, including ingestion, schemas, quality checks, lineage, and governance
Build evals, monitoring, metrics, and regression tests for agentic workflows
Create reusable infrastructure, patterns, and components that other enterprise teams can build on
Partner with system owners and business owners to turn messy enterprise workflows into reliable internal products
Requirements
Strong Python engineering skills for backend services, MCP connectors, agent/tool workflows, eval harnesses, and data ingestion jobs
Strong system design skills across shared infrastructure, app architecture, reliability, and scaling
Experience building internal apps, backend services, APIs, workflow systems, or integration platforms
Understanding of enterprise systems, including controls, approvals, auditability, compliance, and permissions
Practical AI systems experience with RAG, evals, monitoring, MCP/tool use, structured outputs, or multi-agent workflows
Strong data architecture fundamentals, including ingestion, modeling, quality, lineage, and governance
Clear communication with technical stakeholders, system owners, and business owners
High ownership in ambiguous, cross-functional environments
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