GES CAD CATIA Automation & AI/ML Engineer (Python) - ZR_1860_JOB (Position Closed)

Job Title:

GES CAD CATIA Automation & AI/ML Engineer (Python)

• 4 - 7 Years

Job Location:

• Chennai

Job Description:

• We are looking for a seasoned engineer (4-7 years experience) to drive our CATIA automation efforts in Python and architect AI/ML enhancements that elevate CAD-driven design and analysis. You will be the primary hands-on developer for CATIA scripting, mentor junior Python coder, and production deployment of machine-learning and AI solutions that integrate seamlessly with our CAD workflows.

Key Responsibilities:

CATIA Automation (50%)

• Develop and maintain a modular Python framework (PyCATIA, pywin32) for:
  • Parametric Part & Assembly creation, modification, and validation
  • Batch exports (STEP/IGES, meshes, 2D drawings) and feature‐based property injection
  • Custom CLI or lightweight GUI (PyQt/Tkinter) to streamline engineer-driven workflows
• Implement robust error handling, logging, and retry logic for unattended jobs
• Integrate with Teamcenter/ENOVIA REST or ITK APIs to pull/push CAD data and metadata
• Conduct peer code reviews, establish Python style guides, and write unit- and integration-tests

AI/ML & Computer-Vision Integration (50%)

• Lead R&D of ML/AI models that augment CAD automation:
  • Generative design (GANs, autoencoders, topology-optimization surrogates)
  • Predictive performance models (regression, classification, neural nets)
  • Computer-vision QA (feature detection, anomaly flagging in 2D/3D views)
• Extract CAD data (feature trees, meshes, parameters) via Python for ML pipelines
• Package inference services as Dockerized microservices with FastAPI/Flask endpoints
• Define data-collection, monitoring, and retraining strategies to ensure model performance

Mentoring

• Mentor a junior Python coder: lead pair-programming, workshops on COM automation, testing, and ML integration
• Define and enforce best practices across CAD scripts and ML artifacts:
  • Git branching strategies, CI/CD for code and model builds (GitHub Actions, Jenkins)
  • Documentation standards (Confluence, ReadTheDocs) and API references
• Collaborate with mechanical designers, simulation analysts, and PLM admins to gather requirements, validate outputs, and demonstrate ROI
• Metrics, Reporting & Continuous Improvement
  • Reduction in manual CAD hours per part
  • Accuracy and latency of ML predictions
  • Adoption rates and user satisfaction
• Build dashboards (Grafana/Prometheus) to track pipeline health, job success/failure, and resource usage