Industrial AI Engineer & Analyst

Overview The Industrial AI Engineer & Analyst will serve as the organization’s in‑house specialist for industrial artificial intelligence, functioning as a subject-matter expert in the same way a process, mechanical, or electrical engineer would serve as a discipline expert. This role is established to fill a critical capability gap: the ability to rigorously evaluate, validate, and interpret AI solutions offered by external vendors in the industrial domain. The individual will not be expected to design or code AI algorithms. Instead, they will apply engineering judgment, industrial domain knowledge, and working familiarity with AI/ML concepts to determine whether vendor AI tools are technically sound, operationally realistic, and aligned with engineering principles. This role is ideal for someone with an engineering background and real-world industrial experience who has also developed competency in data science, analytics, or AI — either through formal education or professional training. Key Responsibilities AI Application Evaluation & Vendor Validation Act as the internal SME for industrial AI, evaluating vendor AI offerings across process optimization, rotating equipment diagnostics, and other industrial applications. Validate vendor claims, model outputs, and optimization recommendations using engineering principles and operational context. Assess whether AI-driven insights (e.g., anomaly detection, predictive maintenance, optimization scoring) are technically credible and actionable. Identify unrealistic assumptions, data gaps, or engineering inconsistencies in vendor solutions. Industrial Systems & Predictive Maintenance Analysis Support the transition from time-based maintenance to condition-based and predictive maintenance strategies. Analyze sensor and equipment data (vibration, temperature, pressure, chemical signatures, etc.) to confirm whether AI-generated diagnostics align with known system behavior. Work with engineering teams to determine appropriate corrective actions and follow-up evaluations. Data & Model Understanding (Non-Developer Role) Understand the data types, quality requirements, and operational conditions necessary for effective industrial AI. Collaborate with vendors and internal data teams to ensure data sufficiency and relevance. Communicate effectively with data scientists without needing to build models directly. Cross-Functional Collaboration Partner with process, mechanical, reliability, and operations engineers to ensure AI tools support real-world industrial needs. Translate AI outputs into engineering language and operational decision-making. Provide structured feedback to vendors to improve model performance and applicability. Documentation & Reporting Produce clear technical assessments of AI tools, including validation results, limitations, and recommendations. Develop internal frameworks for evaluating future AI technologies. Maintain documentation of model performance, test cases, and engineering interpretations. Required Qualifications Bachelor’s degree in Engineering (Mechanical, Chemical, Electrical, Industrial, or related). Broad industrial experience in plant operations, field engineering, reliability, or similar environments. Working knowledge of AI/ML concepts, data analysis, and predictive modeling (formal training or a master’s in data science is a plus). Strong ability to evaluate technical claims and interpret complex data-driven outputs. Excellent analytical, communication, and critical-thinking skills. Preferred Qualifications Experience with condition-based or predictive maintenance programs. Familiarity with industrial sensors, control systems, and equipment diagnostics. Exposure to data science tools or workflows (Python, SQL, dashboards) at a conceptual or applied level. Prior experience interfacing with technology vendors or evaluating external technical solutions. Work Arrangement Primary expectation: Full-time, on-site. No formal hybrid or remote program is currently offered. May be structured as part-time initially, if requested by the candidate. Ideal Candidate Profile The strongest candidates will: Have an engineering degree and real-world industrial/field experience. Possess additional training or education in data science, analytics, or AI. Be comfortable acting as an internal SME for industrial AI—similar to a discipline engineer. Have the ability to challenge vendor claims, validate AI outputs, and ensure engineering rigor. Thrive at the intersection of engineering intuition and data-driven insights. #J-18808-Ljbffr Novetus Engineering