Senior ML Engineer - Hybrid, Scale ML for Impact

Publicis Groupe Holdings B.V

Chicago (IL)

Hybrid

USD 117,000 - 160,000

Full time

3 days ago
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Benefits offered by this job

Learning opportunities
Competitive pay
Work-life balance
Health benefits
Paid holidays
Wellness program

Job summary

Publicis Sapient in Chicago Hybrid is seeking a Senior Associate Data Scientist responsible for developing and deploying machine learning models at scale. You will work with cross-functional teams to design data-driven solutions that drive business impact.

You will optimize ML pipelines, ensure production reliability, and apply best practices in preprocessing, feature engineering, and model evaluation, using AWS, GCP, or Azure to train, deploy, and monitor models.

Qualifications

  • 5+ years of experience in ML, data science, or AI engineering.
  • Proven ability to design, deploy, and optimize ML models.
  • Experience with ML frameworks and cloud platforms.

Responsibilities

  • Design, develop, and deploy ML models at scale.
  • Collaborate with data engineers and product managers on end-to-end ML solutions.
  • Optimize pipelines for performance and cost efficiency.
  • Ensure reliability and maintainability of AI systems in production.
  • Use cloud platforms (AWS/GCP/Azure) for training and deployment.
  • Implement best practices in data preprocessing and model evaluation.
  • Stay updated with ML engineering advancements.

Skills

ML engineering
Python
TensorFlow/PyTorch
MLOps
Cloud ML platforms

Tools

AWS SageMaker
GCP AI Platform
Azure ML
CI/CD pipelines
Model monitoring

Job description

Publicis Sapient in Chicago Hybrid is seeking a Senior Associate Data Scientist responsible for developing and deploying machine learning models at scale. You will work with cross-functional teams to design data-driven solutions that drive business impact.

You will optimize ML pipelines, ensure production reliability, and apply best practices in preprocessing, feature engineering, and model evaluation, using AWS, GCP, or Azure to train, deploy, and monitor models.

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