Job Summary
The Software Developer will support the development and production of artificial intelligence products within a Manufacturing IT environment. The role will focus on AI-driven software engineering, with an emphasis on React and Python development and the use of AI tools to accelerate software delivery. The position will contribute to solutions that provide analytics and operational insights for plant-floor leaders across a global manufacturing environment, including staffing modules designed to help optimize plant operations. The role will also support development operations, release management, feature testing, and product reliability.

Required Qualifications
• 7+ years of experience in Infrastructure, SRE, or Core Platform Engineering with a strong focus on distributed systems.
• Strong AI-driven development experience, including AI engineering and coding.
• Strong software engineering experience with React and Python.
• Ability to leverage AI tools to accelerate software development.
• Advanced knowledge of Kubernetes and Helm, including the Kubernetes control plane, custom controllers/CRDs, and tuning stateful sets or long-lived connection routing.
• Expert-level production Python experience, including Python memory management, asyncio event loops, and tuning ASGI servers for high-concurrency gateway flows.
• Proven experience with advanced load testing using k6 or Locust for stateful systems, dynamic session tokens, and asynchronous backend workers.
• Hands-on experience with CI/CD automation using GitHub Actions targeting Azure Container Registry (ACR) or similar enterprise registries.
• Experience with Azure Kubernetes Service (AKS).
• Deep conceptual understanding of LLM infrastructure, including tool calling, agent memory architectures, state graphs, and operational differences between token-streaming and unary API calls.

Preferred Qualifications
• Production experience with service mesh technologies such as Istio, Linkerd, or Cilium for advanced A2A traffic splitting, mutual TLS (mTLS), and zero-trust enforcement.
• Experience with secure container runtimes such as gVisor or Kata Containers for running unverified user-generated tool code or agent scripts.
• Experience with chaos engineering, including injecting failures into asynchronous message queues or simulating LLM API timeouts to validate graph recovery safety.