Key Responsibilities

• Model Development: Design AI surrogate models using Graph Convolutional Neural Networks (GCNNs) to support or replace physics-based computer-aided engineering (CAE).
• Lifecycle Ownership: Manage data ingestion, feature engineering, model training, evaluation, deployment, and performance monitoring.
• Cloud Architecture: Build and deploy scalable cloud-based AI systems on platforms like AWS or Azure.
• MLOps Implementation: Apply MLOps and GenAIOps best practices, including version control, CI/CD pipelines, and data drift detection.
• Workflow Automation: Develop agentic AI solutions to execute, augment, and monitor engineering workflows. [1]

Qualifications & Skills

• Education: Bachelor’s, Master’s, or Ph.D. in Computer Science, Engineering, Data Science, or a related field.
• Experience: 4 to 8+ years of relevant experience in developing and deploying machine learning or AI systems in production environments.
• Programming: Advanced proficiency in Python and experience with frameworks like PyTorch or TensorFlow.
• Cloud & Tools: Hands-on experience with cloud platforms (AWS/Azure), containerization (Docker), and orchestration or agent tooling (LangChain, LangGraph)