Software Engineer, CrewAI

• Strong Python backend/platform engineering experience, especially building production services rather than only libraries
• Experience with FastAPI or similar API frameworks, Celery or other job systems, Redis, Pydantic, and typed Python
• Good instincts for distributed systems: retries, idempotency, async execution, status tracking, race conditions, and failure recovery
• Comfort with auth and security-sensitive systems: JWTs, webhooks, signatures, secrets, IAM/workload identity, and least-privilege thinking
• Practical observability experience: tracing, structured logging, metrics, Sentry/OpenTelemetry, and debugging multi-service failures
• Ability to work at the boundary between an open-source framework and a hosted enterprise platform without creating brittle coupling
• Strong testing habits and comfort with CI, package/version management, and release discipline

Desirable

• You'll work on the enterprise runtime layer that turns CrewAI's open-source Crews and Flows into secure, observable, remotely executable production systems
• This is the layer between the framework and the platform: APIs, workers, checkpoints, webhooks, auth, deployment behavior, telemetry, and enterprise extensions that make CrewAI run reliably in real customer environments
• You'll partner closely with the open-source, product, and infrastructure teams, but your center of gravity is production execution: making agent workflows resumable, inspectable, authenticated, observable, and safe to operate at scale
• Build and maintain the Python enterprise runtime around CrewAI: FastAPI services, Celery workers, Redis-backed state, execution APIs, and deployment-facing tools
• Extend open-source CrewAI behavior for enterprise environments while preserving compatibility with upstream framework changes
• Own production execution flows: crew and flow kickoff, status, retries, cancellation, checkpoint restore and fork, chat/session state, and human-in-the-loop resume paths
• Build secure integration surfaces: JWT auth, signed webhooks, token refresh, file handling, secret fetching, and workload identity across AWS, GCP, and Azure
• Improve observability across distributed execution: OpenTelemetry traces, structured logs, Sentry, event tracking, and debuggability across API, worker, and platform boundaries
• Maintain strong test coverage for async/runtime behavior using pytest, mypy, ruff, mocks/fakes, and e2e deployment harnesses
• Partner with the Agent Management Platform team on API contracts, versioning, enterprise client behavior, deployment status, and failure reporting