General Motors

Senior/Lead Engineer, Virtual Engineering, AI/ML

General Motors

full-time

Posted on:

Origin:  • 🇮🇳 India

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Job Level

Senior

Tech Stack

AWSAzureCloudETLNoSQLPythonPyTorchScikit-LearnSQLTensorflow

About the role

  • Leverage Machine Learning methodologies to improve Manufacturing Engineering and Operations processes.
  • Execute end-to-end projects from ideation to deployment, applying relevant Tools and Methods in ML and data analytics to solve Manufacturing problems while ensuring data security and delivering measurable impact.
  • Collaborate with stakeholders to understand business problems in the in the Manufacturing Engineering and Operations space and solve them using ML methodologies.
  • Design, develop, and fine-tune AI/ML models for classification, regression, clustering, and recommendation systems.
  • Work with MLOps tools to automate workflows, CI/CD pipelines, and model monitoring.
  • Evaluate, validate, and benchmark model performance using appropriate metrics.
  • Deploy AI models into production environments in collaboration with IT/AI teams.
  • Establish monitoring and maintenance processes to ensure model accuracy over time.
  • Ensure that all AI solutions comply with organizational data security, confidentiality, and regulatory requirements.
  • Document workflows, results, and lessons learned for organizational knowledge sharing.
  • Stay updated on advancements in ML model evaluation, ML frameworks, end-to-end ML pipelines.

Requirements

  • Bachelor’s or Masters Degree Mechanical/Automobile/Production /Mechatronics Engineering discipline or similar.
  • 5+ years in Automotive Manufacturing / Manufacturing Engineering Experience.
  • 1+ year experience in implementing AI/ML solutions in Automotive use cases.
  • Should have executed at least 2 end-to-end projects in the text or Image data domain (from problem definition to deployment).
  • Strong programming skills in Python
  • Proficiency with ML/DL frameworks like Scikit-learn, TensorFlow, PyTorch, XGBoost.
  • Solid understanding of statistics, probability, and linear algebra.
  • Experience in data preprocessing, feature engineering, ETL and Exploratory Data Analysis (EDA).
  • Experience with MLOps platforms (MLflow, Kubeflow, Vertex AI, Azure ML)
  • Knowledge of ML model evaluation
  • Experience with SQL/NoSQL databases and handling large datasets.
  • Strong problem-solving and analytical mindset.
  • Understanding of data annotation tools and MLOps workflows.
  • Experience in domain-specific AI use cases (manufacturing, automotive, etc.).