Software Engineer, AI Agent Platform

ChampionAI is QAD | Redzone agentic platform, purpose-built for manufacturing and utilized by the various business units within QAD | Redzone. This engineer joins the core platform team with three main areas of focus: building new capabilities into the platform, helping Applied AI teams at partner business units build and ship Champions correctly, and directly building Champion agents for business-unit use cases when needed. You will work in the same codebase as the Applied AI engineers you support, which keeps the enablement work practical and the platform work focused on real problems. Key Responsibilities Platform Development Build and ship features across the Champion platform repositories  Improve developer experience: tooling, scaffolding, internal documentation, and onboarding paths for Applied AI engineers. Maintain and evolve the MCP tool server and agent infrastructure that BU teams depend on. Identify and address friction points that slow down Champion development or deployment Applied AI Enablement Support BU Applied AI engineers in building, deploying, and operating Champions correctly Review agent implementations for prompt quality, scope enforcement, auth configuration, and deployment setup Contribute to internal engineering guides and skill documentation Pair with BU engineers on first K8S manifest creation, database registration, and LaunchDarkly prompt rollout Agent Development Design and build Champion agents for BU use-cases when the platform team is directly engaged Write and iterate on system prompts, tool bindings, and context injection for production agents Register agents in Champion Server and configure LaunchDarkly-gated prompt rollout across environments Engineering Practices We follow trunk-based development with PR-gated merges to main. Engineers are expected to: Write tests before implementing. TDD is the expectation, not a nice-to-have. Keep PRs small and focused; use feature flags to ship partial work incrementally Follow conventional commit format (feat:, fix:, etc.) Be mindful of API and schema backward-compatibility; prefer additive changes over breaking ones Review your own diff before requesting review   Core Requirements Python: Proficient in async Python, Pydantic, type hints, and FastAPI. Experience with the Strands agent SDK or a comparable agentic framework. Testing is non-optional: candidates should be comfortable with pytest, pytest- asyncio, and DeepEval for agent-specific evaluation. We test agent behavior, not just unit logic. Prompt Engineering: Able to write and iterate on production system prompts: XML-structured, scope-enforced, with tool descriptions that guide LLM delegation reliably. Model Context Protocol (MCP): Solid understanding of MCP and hands-on experience building or consuming MCP servers. Familiarity with Agent-to-Agent (A2A) protocol is a strong plus. Agent Patterns: Familiar with multi-agent architectures and orchestration patterns…