Sr. AI Automation Engineer

Basic Qualifications

• BS in Industrial/Mechanical/Chemical Engineering from an accredited institution
• 5 years manufacturing experience
• Up to 50% travel
• Employees must be legally authorized to work in the United States. Verification of employment eligibility will be required at the time of hire. Visa sponsorship is not available for this position.

Preferred Qualifications

• Extensive Investment Casting domain knowledge
• Familiarity with Six Sigma, Lean, and basic data tools (Python/Tableau a plus)

This role designs, documents, and continuously improves investment casting manufacturing processes by combining deep domain expertise in Process Engineering with AI-enabled analytics to reduce waste, improve throughput, and shorten cycle times. The position collaborates closely with the AI deployment team to ensure production data is clean and usable for model training, then pilots and implements AI-driven process control and problem-solving on the factory floor. The role also ensures all changes meet safety, quality, and regulatory requirements while delivering measurable business outcomes.

Job Roles

• Domain Expertise--maintains deep knowledge of internal processes and capabilities, emerging technologies, enabling elements (raw materials, inputs, science, etc.)
• Innovation and improvement--willing to try new things; focused on continuous improvement even for persistent problems; constantly learning
• Development focused--provides guidance and assistance to associates; helps others improve in their roles; distributes work through a growth lens
• Data informed--focused on things that drive quantifiable business outcomes; evaluates based on quantitative feedback; knows and explains the relationship between actions and expected results
• Prioritization and orchestration--differentiates between urgent and important; attends to issues as they arise within the framework of a strategy and plan; focused on team deployment toward solutions
• Relationship and communication--connects individually and with teams across practice areas and leadership levels; builds rapport and commonality; creates a "win together" ethic; gives and receives feedback and takes action accordingly
• Systems thinker--sees the business and its operation holistically; understands actions and reactions; manages complexity well; understands when to intervene and when to step back
• Independent initiative--manages time well; does not require direction; takes responsibility for finding and solving problems; optimizes based on learning and team input; thinks about the future and anticipates needs
• Customer focus--understands requirements and applications to ensure an excellent product; manages multiple products; leverages historical knowledge and data; takes on difficult configurations and manages them professionally and objectively

Responsibilities

• Design, document, and optimize manufacturing processes, incorporating AI analytics for efficiency gains, waste reduction, and cycle-time improvements.
• Analyze real-time production data (from sensors/IoT) to identify bottlenecks and recommend AI model-driven changes.
• Provide manufacturing domain knowledge as it relates to process engineering (materials, processes, KIV-KOV relationships), to guide AI model development, feature selection, and validation of AI recommendations.
• Collaborate with AI engineers to ensure processes generate clean, labeled data for training models; implement AI recommendations on the factory floor.
• Lead pilot projects for AI-based process control and agentic problem solving (e.g., waste / scrap reduction) and validate ROI.
• Ensure compliance with safety, quality, and regulatory standards while introducing AI tools.