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 jobsIntegrate with Teamcenter/ENOVIA REST or ITK APIs to pull/push CAD data and metadataConduct 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 pipelinesPackage inference services as Dockerized microservices with FastAPI/Flask endpointsDefine 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 integrationDefine 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 ROIMetrics, 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