The Data & AI Engineer will develop data pipelines, predictive models, and dashboards while collaborating with software teams to integrate insights into engineering processes.
We’re seeking a Data & AI Engineer to develop intelligent data pipelines and analytics solutions that power smarter decisions across silicon design, verification, and manufacturing. You’ll transform engineering data into actionable insights through automation, modeling, and visualization.
Responsibilities
- Build and maintain data pipelines to support machine learning and analytics workflows.
- Collect, clean, and transform large, complex datasets from engineering environments.
- Develop and train predictive models for yield, performance, and anomaly detection.
- Automate recurring data analysis tasks and integrate models into engineering processes.
- Collaborate with design and software teams to embed AI-driven insights into products.
- Create dashboards and visualization tools for reporting and decision-making.
- Document code, models, and processes for transparency and reproducibility.
Qualifications
- Proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn).
- Strong skills in data manipulation (Pandas, NumPy, SQL).
- Experience with workflow orchestration (Airflow, Spark, or similar).
- 3–5 years of experience in data engineering or applied AI.
- Bachelor’s degree in Electrical Engineering, Computer Science, or related field.
Preferred / Plus
- Familiarity with semiconductor design, verification, or manufacturing datasets.
- Understanding of statistical modeling and predictive maintenance.
- Experience with cloud environments (AWS, Azure, GCP) and version control (Git).
- Knowledge of MLOps principles (deployment, monitoring, CI/CD).
Top Skills
Airflow
AWS
Azure
GCP
Git
Numpy
Pandas
Python
PyTorch
Scikit-Learn
Spark
SQL
TensorFlow
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