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Senior Engineering AI / Advisory Lead

Tek Analytics, LLC

Job
29895
Posted
Location
Chicago, IL
Work type
Contract
Tax terms
W2, C2C, 1099
Experience
Experience open
Openings
1 opening

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Skills

  • 3D Computer Graphics
  • 3D Modeling
  • Asset Management
  • Artificial Intelligence
  • Bill Of Materials
  • AutoCAD
  • Computer Vision
  • Computer Science
  • Drawing
  • Engineering Design
  • Data Science
  • Enterprise Asset Management
  • Enterprise Resource Planning
  • Lifecycle Management
  • Extraction
  • Generative Artificial Intelligence (AI)
  • Industrial Engineering
  • Integration Testing
  • Scada
  • Quality Inspection

About the job

Role Summary

Lead Engineering AI advisory and delivery engagements across industrial engineering, manufacturing, and asset-intensive environments. Own portfolio discovery, solution maturity assessment, technical architecture, AI/ML solution development direction, industrial integration, deployment, and ongoing improvement of Engineering AI solutions.

Key Responsibilities

· Lead discovery workshops and assess existing Engineering AI solutions, prototypes, production applications, and roadmap initiatives.

· Evaluate solution maturity, functional depth, adoption, architecture, AI/ML approaches, data pipelines, integration patterns, and production readiness.

· Identify technical gaps, bottlenecks, improvement opportunities, reusable components, and capability requirements.

· Define target-state architectures, technical roadmaps, implementation priorities, and solution governance.

· Lead technical direction for industrial AI use cases, including predictive maintenance, engineering drawing intelligence, automated BOM extraction, engineering knowledge graphs, 3D model intelligence, digital twins, and AI-powered quality inspection.

· Guide the application of AI/ML, computer vision, GenAI, local LLMs, RAG, and agentic AI to industrial engineering problems.

· Assess and guide solutions involving AVEVA, AutoCAD, Hexagon, DXF, engineering drawings, 3D models, drone/scan data, point clouds, and pipe-routing workflows.

· Define integration approaches across industrial engineering platforms, PLM, ERP, MES, EAM, PLC/SCADA, and industrial data systems.

· Lead technical design reviews, model validation, integration testing, deployment readiness, and resolution of complex technical issues.

· Establish production monitoring, maintenance, model improvement, and continuous optimization practices.

· Identify required specialist skills, delivery capabilities, and reusable engineering AI components; guide technical teams and client stakeholders.

Required Skills

· Strong expertise in AI/ML solution architecture and applied AI, with working knowledge of GenAI, LLMs, RAG, computer vision, and agentic AI.

· Strong understanding of industrial engineering, manufacturing, asset management, or engineering design workflows.

· Experience with industrial engineering software and integration patterns, preferably AVEVA, Hexagon, AutoCAD, or comparable platforms.

· Understanding of engineering data, CAD/DXF, 3D models, engineering BOMs, industrial data pipelines, and enterprise/operational systems.

· Experience assessing model accuracy, robustness, edge cases, scalability, observability, security, and production readiness.

· Knowledge of deployment architecture, MLOps, monitoring, maintenance, and solution lifecycle management.

· Strong technical advisory, architecture, problem-solving, delivery leadership, and stakeholder management capabilities.

Qualifications

· Bachelor's or Master's degree in Engineering, Computer Science, Data Science, or a related discipline.

· 16-20+ years of relevant technology experience, including substantial experience in AI/ML, industrial digital solutions, or solution architecture.

· Demonstrated ownership of technical delivery from discovery and solution design through implementation, deployment, and maintenance.

· Experience leading technical assessments, architecture reviews, and complex industrial or enterprise technology initiatives.

Expected Capabilities

Lead structured discovery and maturity assessments; define technical direction and roadmaps; guide specialized Engineering AI implementations; and own architecture, technical quality, delivery outcomes, and production readiness.

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