Senior ML Engineer — Hybrid, Scalable AI Solutions

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

Benefits offered by this job

Learning opportunities

Competitive compensation

Flexible work-life balance

Comprehensive health benefits

Generous paid leave

Wellness programs

Job summary

Publicis Sapient in Chicago is seeking a Senior Associate Data Scientist to design, develop, and deploy ML models at scale across cross-functional teams.

You will own end-to-end ML solutions, optimize pipelines, and ensure production reliability while leveraging cloud platforms (AWS, GCP, Azure) for training and monitoring. This role emphasizes ML engineering excellence and impact across the business.

Qualifications

• 5+ years of experience in ML, data science, or AI engineering.
• Strong expertise in ML model development, deployment, and optimization.
• Proficiency in Python, TensorFlow, PyTorch, or other ML frameworks.
• Experience with MLOps, CI/CD pipelines, and model monitoring.
• Hands-on experience with cloud-based ML platforms (AWS SageMaker, GCP AI Platform, Azure ML).
• Strong understanding of data engineering, data pipelines, and big data technologies.
• Excellent problem-solving and analytical skills.

Responsibilities

• Design, develop, and deploy machine learning models at scale.
• Collaborate with data engineers, product managers, and business stakeholders to implement end-to-end ML solutions.
• Optimize and scale machine learning pipelines for performance and efficiency.
• Ensure the reliability, scalability, and maintainability of AI/ML systems in production.
• Utilize cloud-based platforms for model training, deployment, and monitoring.

Skills

ML engineering

Model development

Python

TensorFlow

PyTorch

MLOps

CI/CD

Model monitoring

Cloud ML platforms

AWS SageMaker

GCP AI Platform

Azure ML

Data engineering

Tools

Kubernetes

Docker

Job description

Publicis Sapient in Chicago is seeking a Senior Associate Data Scientist to design, develop, and deploy ML models at scale across cross-functional teams.

You will own end-to-end ML solutions, optimize pipelines, and ensure production reliability while leveraging cloud platforms (AWS, GCP, Azure) for training and monitoring. This role emphasizes ML engineering excellence and impact across the business.