Software Engineer, AI Agent Platform

QAD

5d • Remote

About The Position

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. 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. We run an AI-native SDLC. AI-assisted tools (Claude Code, Cursor) are part of the standard engineering workflow at every stage: planning, implementation, review, and documentation. This is not optional or supplemental; it is how we work. Candidates should be comfortable with AI-assisted development and willing to invest in getting good at it. Use Claude Code and Cursor actively across planning, implementation, and code review Know when AI output is wrong and push back on it. Help drive adoption across the team by sharing what works. This is not an ML research or data science role. The platform team owns cluster networking, observability pipelines, auth configuration, and LLM routing, so deep expertise in those areas is not required. The job is building reliable software on top of that infrastructure and helping others do the same.

Requirements

• 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.
• Able to write and iterate on production system prompts: XML-structured, scope-enforced, with tool descriptions that guide LLM delegation reliably.
• Solid understanding of MCP and hands-on experience building or consuming MCP servers.
• Familiar with multi-agent architectures and orchestration patterns beyond basic ReAct: supervisor/subagent delegation, parallel tool use, handoffs, and context management across agent boundaries.
• Able to design and run evaluation suites for agent behavior: correctness checks, scope enforcement tests, regression coverage, and systematic prompt iteration based on eval results.
• Comfortable authoring Dockerfiles, multi-stage builds, and local dev environments via docker-compose / make.
• Comfortable with templated INSERT/SELECT, foreign key relationships, and reading an ER diagram.
• Follows conventional commit format (feat:, fix:) and a PR-based trunk workflow.

Nice To Haves

• Familiarity with Agent-to-Agent (A2A) protocol is a strong plus.
• Working knowledge of Deployments, Services, ConfigMaps, and ServiceAccounts. Able to read and adapt K8s manifests and use kubectl for basic troubleshooting.
• Working knowledge of ECR, EKS, IAM, and Bedrock (inference layer).
• Conceptual understanding of Authorization Code Flow with PKCE, token exchange, and agent auth delegation.
• Beginner-level familiarity; able to make targeted changes to existing modules and interpret a plan diff.
• Experience managing feature flags or AI config overrides for environment-gated rollout.
• AWS Textract or comparable pipeline experience for use cases involving structured document extraction.
• Comfortable with AI-assisted development and willing to invest in getting good at it.
• Use Claude Code and Cursor actively across planning, implementation, and code review
• Know when AI output is wrong and push back on it.
• Help drive adoption across the team by sharing what works

Responsibilities

• 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
• 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
• 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

Benefits

• We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity, status as a veteran, and basis of disability or any other federal, state or local protected class.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed