Python Rule Engine Systems Developer

I’m building a distributed automation platform that leans heavily on rule-based decision making and needs to run cleanly across mixed virtualised and containerised environments. Your core mission is to design and implement the Python services that drive these rules, expose them through clean APIs, and make sure they scale on both CPU- and GPU-backed nodes.

Must-have expertise
• Rule engines we actively use: PyKnow, Experta, and Drools
• VM layers you’ll manage: KVM, Proxmox, VMware
• Orchestration stack: Docker, Podman, Kubernetes

Beyond those essentials, the work touches GPU workloads, message queues (Redis, RabbitMQ, Kafka), Postgres/MySQL/MongoDB, and automation pipelines, so practical experience in at least several of these areas will help you hit the ground running.

What I expect from you
1. Build the Python rule services, wired into the message queue layer.
2. Create REST/JSON APIs so external apps can trigger, modify, and monitor rules.
3. Provide Terraform/Ansible (or comparable) scripts that spin up VMs, schedule containers, and deploy updates with zero downtime.
4. Optimise for GPU tasks when a node advertises CUDA.
5. Document the entire flow clearly: architecture diagrams, setup steps, and example calls.
6. Deliver a full test suite that covers rule correctness, scaling behaviour, and fail-over scenarios.

Acceptance is straightforward: I’ll spin up the provided scripts on a fresh cloud account, run the tests, and verify that a sample rule set executes end-to-end across at least two VMs and three containers.

If you thrive on complex, multi-layered Python systems and enjoy seeing rules spring to life in production, let’s talk.