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Lumbra is building Nebula, an agentic harness operating in environments where analysts need to pull intelligence from diverse, often restricted data streams, and where the harness itself must deploy into on-prem cloud and fully air-gapped environments with limited or no internet connectivity. We're looking for a cleared software engineer to own the integration layer: building connectors to external systems, extending our agent tool framework, and ensuring Nebula works reliably in disconnected classified environments.
Lumbra is building Nebula, an agentic harness operating in environments where analysts need to pull intelligence from diverse, often restricted data streams, and where the harness itself must deploy into on-prem cloud and fully air-gapped environments with limited or no internet connectivity. We're looking for a cleared software engineer to own the integration layer: building connectors to external systems, extending our agent tool framework, and ensuring Nebula works reliably in disconnected classified environments.
Build and maintain integration tools in Python that connect agents to external data sources, APIs, and analyst workflows. You need strong Python skills and experience writing reliable, well-tested connectors that handle authentication, rate limiting, and error recovery.
Extend and create MCP servers with new tool registrations, resource types, and transport mechanisms.
Develop connectors for collaboration platforms and communication channels, enabling agents to interact with analysts in the tools they already use.
Maintain deployment parity by ensuring all new features, environment variables, and configurations work across commercial cloud, on-prem cloud, and air-gapped environments. You'll work closely with the infrastructure team to keep all tracks aligned.
Write thorough tests for all integration code, covering error cases, network failures, and edge conditions in disconnected environments. You'll follow a contract-driven testing approach with clear coverage expectations.
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Support onsite deployments by troubleshooting issues in air-gapped Kubernetes clusters where standard debugging tools aren't available. Resourcefulness and self-sufficiency are essential.
Care deeply about integration performance and reliability : connection pooling, retry efficiency, parsing throughput, and end-to-end latency from data source to agent. In constrained environments, every wasted cycle and unnecessary round-trip compounds.
Ensure all integration code meets security standards for classified environments : no hardcoded secrets, proper credential handling, and audit-ready practices throughout.
Building AI orchestration frameworks for secure intelligence environments.
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Senior
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