Senior Engineer - Virtual Engineering - AI ML

Sponsorship: GM DOES NOT PROVIDE IMMIGRATION-RELATED SPONSORSHIP FOR THIS ROLE.

DO NOT APPLY FOR THIS ROLE IF YOU WILL NEED GM IMMIGRATION SPONSORSHIP (e.g., H-1B, TN, STEM OPT, etc.) NOW OR IN THE FUTURE.

Work Arrangement: This role is categorized as hybrid.

This means the successful candidate is expected to report to the office four times per week or other frequency dictated by the business.

Senior Engineer / Lead Engineer – ML will 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.

Bachelor’s or Masters Degree Mechanical/Automobile/Production /Mechatronics Engineering discipline or similar.

4- 8 years in Automotive Manufacturing / Manufacturing Engineering Experience.

2+ 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.

in data preprocessing, feature engineering, ETL and Exploratory Data Analysis (EDA).

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.

in domain-specific AI use cases (manufacturing, automotive, etc.).

What Will Give You A Competitive Edge (Preferred Qualifications)

Knowledge of deep learning architectures (CNNs, RNNs, Transformers).

Familiarity with cloud-based platforms (Azure, AWS).

in distributed training and scaling ML on large datasets.

Strong problem-solving mindset and curiosity for AI innovation.

Ability to translate domain problems into AI solutions.

Collaboration skills to work with cross-functional teams.

Clear communication of technical concepts to non-technical stakeholders.