Senior ML Engineer – Hybrid, Build & Scale AI Solutions

Publicis Sapient; Chicago (IL); Hybrid; USD 117,000 - 160,000; Full time

Benefits offered by this job

Access to ongoing learning

Competitive compensation and benefits

Flexible work-life balance

Comprehensive health benefits

Generous paid leave and holidays

Job summary

Publicis Sapient in Chicago is seeking a Senior Associate Data Scientist to design, develop, and deploy machine learning models at scale. You will work with cross-functional teams to implement data-driven solutions that drive business impact and continuously optimize production ML pipelines.

The role emphasizes ML engineering, cloud-based training and monitoring, and staying current with advances in AI technologies to deliver robust systems.

Qualifications

• 5+ years in ML, data science, or AI engineering.
• Proficiency in Python and ML frameworks.
• Experience with MLOps, CI/CD, and model monitoring.
• Hands-on with cloud-based ML platforms (AWS SageMaker, GCP AI Platform, Azure ML).
• Strong data engineering, pipelines, and big data knowledge.
• Excellent problem-solving and analytical skills.

Responsibilities

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

Skills

ML experience

Python

TensorFlow

PyTorch

MLOps

CI/CD

Model monitoring

Cloud ML platforms

Data engineering

Big data

Problem solving

Tools

Kubernetes

Docker

Job description

Publicis Sapient in Chicago is seeking a Senior Associate Data Scientist to design, develop, and deploy machine learning models at scale. You will work with cross-functional teams to implement data-driven solutions that drive business impact and continuously optimize production ML pipelines.

The role emphasizes ML engineering, cloud-based training and monitoring, and staying current with advances in AI technologies to deliver robust systems.