Job Description

Job Title:

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

Experience:

  • 4 - 7 Years

    • Qualification:

  • B.E/B.Tech or M.E/M.Tech (Automotive & Mechanical)

    • 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

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