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Medium — niche agent and prompt-engineering skills offset general mid-level ML engineer demand at an established company.
Medium — core Python and platform skills transfer, but MCP/A2A and agent-specific experience bias toward AI-focused backgrounds.
High — explicit 2+ years plus many mandatory technical skills and AI-agent-specific requirements.
Develop and deliver new features and platform capabilities for QAD | Redzone's ChampionAI agentic platform using trunk-based development.
Support business unit Applied AI teams by reviewing, deploying, and operating production Champion agents and solving deployment or development bottlenecks.
Design and build Champion agents for specific business use cases, including writing and iterating on system prompts and tool integrations.
Minimum 2 years of hands-on software development experience specializing in Python and AI-driven systems.
Proficiency in Python (including async, FastAPI, Pydantic, pytest), Docker, PostgreSQL, Git, and knowledge of AI agent protocols like MCP and A2A.
Bachelor's degree in Computer Science, Engineering, or related field (or equivalent practical experience).
Work Experience Required: 2+ years explicitly mentioned.
Experienced in AI agent orchestration, prompt engineering, and multi-agent architectures with production-grade deployment skills.
Comfortable working closely with cross-functional AI teams on Kubernetes, LaunchDarkly feature flags, and production infrastructure.
Strategic fit includes familiarity with manufacturing domain software and AI-native development tools such as Cursor, Claude Code, and DeepEval.