AI Process Engineer

GE Aerospace is a leading company in the aerospace sector, seeking an AI Process Engineer to enhance their operational efficiency. The role involves identifying and modernizing manual processes through AI, ensuring expert knowledge is effectively captured and utilized in automated systems.

Responsibilities:

• Process Discovery & Knowledge Extraction
• Embed with domain experts to observe and document actual workflows (not the documented ones)
• Distinguish genuine expertise and edge-case reasoning from ritual, habit, and cargo-cutting
• Identify where "art" is pattern recognition that can be modeled, versus true judgment calls requiring human decision-making
• Produce formal process models from informal, oral-tradition knowledge
• Tool Replacement & Modernization:
• Audit legacy/bespoke tooling — identify what they do vs. what people think they do
• Design replacements using AI-native approaches (LLM pipelines, vision models, structured extraction) where appropriate, and conventional engineering where not
• Manage graceful deprecation of legacy tools without disrupting active operations
• Agent Workflow Design & Orchestration:
• Architect multi-step AI agent workflows that decompose complex expert tasks into verifiable stages
• Define tool-use patterns, context management, and failure/fallback strategies for agents
• Build evaluation frameworks that compare agent output against expert baselines
• Implement confidence-gated escalation — the system knows what it doesn't know
• Human-in-the-Loop Architecture:
• Design the HITL topology: which decisions require human approval, review, or override
• Define escalation thresholds, audit trails, and feedback loops that improve the system over time
• Ensure experts transition from "doers" to "reviewers and teachers" without loss of engagement or institutional knowledge
• Build calibration mechanisms so human reviewers stay sharp (not rubber-stamping)

Requirements:

• Bachelor's degree from an accredited university or college
• Minimum of 5 years of experience in software/AI engineering with at least 2 years building LLM-based or agent-based systems
• Demonstrated ability to extract tacit knowledge from domain experts (process mining, cognitive task analysis, or equivalent)
• Experience designing and deploying AI agent orchestration (multi-step, tool-using, with evaluation)
• Strong systems thinking — can model a process end-to-end before writing a line of code
• Track record of replacing legacy systems without burning the house down
• Background in knowledge engineering, expert systems, or decision support
• Experience in Aerospace Industry or Aviation Regulatory organizations
• Familiarity with regulated or safety-critical environments where auditability matters