Minimum qualifications:

• Bachelor's degree in Computer Science, Artificial Intelligence, Engineering, Data Science, or a related field, or equivalent practical experience.
• 2+ years of software engineering, AI engineering, backend integration, or workflow systems experience.
• Experience building APIs, workflow tools, AI prototypes, orchestration logic, graph-based systems, or data-driven applications.
• Comfortable using Git, JSON, databases, HTTP APIs, and cloud-based development workflows.
• Ability to move from workflow idea to working implementation with clear documentation.

Job Description:

As an AI Workflow Engineer at Aiotrix, you will design the graph-based logic that powers intelligent workflows inside ART. This role focuses on state machines, agentic directed acyclic graphs, modular blocks, tool nodes, runtime transitions, and multi-agent orchestration patterns.

You will work on the internal workflow engine concepts that allow users to compose, test, reuse, and execute intelligent flows, making complex agent behavior understandable and controllable.

Responsibilities:

• Design and implement workflow graph logic using state machines, DAGs, tool nodes, conditional branches, retries, triggers, actions, and structured outputs.
• Build reusable ART workflow blocks for agents, assistants, document intelligence, tool execution, approvals, and multi-step reasoning patterns.
• Define node contracts, edge behavior, state transitions, execution rules, block metadata, and reusable workflow templates.
• Work with orchestration frameworks and concepts such as LangGraph-style flows, multi-agent coordination, event-driven execution, and custom runtime logic.
• Create workflow test cases, trace graph execution, debug failure paths, and improve task completion reliability.
• Implement validation layers, approval nodes, fallback branches, interruption handling, and human-in-the-loop checkpoints.
• Collaborate with product, AI, backend, and frontend teams to turn workflow graph logic into usable ART builder capabilities.
• Document workflow architecture, node behavior, state models, tool contracts, execution paths, and failure cases clearly.

Preferred qualifications:

• Experience with Python, JavaScript, TypeScript, APIs, databases, and backend integration patterns.
• Hands-on exposure to LLM APIs, tool calling, structured outputs, stateful workflows, or agent orchestration.
• Understanding of state machines, DAGs, graph execution, node/edge contracts, triggers, retries, approvals, and runtime transitions.
• Experience with LangGraph, LangChain, LlamaIndex, workflow engines, event-driven systems, or custom orchestration layers is a plus.
• Ability to break complex agent behavior into modular, reusable workflow blocks.
• Strong debugging mindset and ability to reason about graph reliability, failure paths, edge cases, and user control.