Senior ML Platform Engineer

Tech Economy; Chicago (IL); Hybrid; USD 141,000 - 169,000; Full time

Benefits offered by this job

Medical, dental, vision coverage

Fully vested 401(k)

Paid time off and holidays

Fitness reimbursements

Job summary

Bain & Company is hiring a Staff Engineer, Machine Learning to define architecture and operational excellence for scalable ML platforms. You will lead production deployment of LLM-powered systems and collaborate with cross-functional teams to deliver reliable, high-impact AI solutions.

The role emphasizes mentoring engineers, establishing CI/CD and MLOps practices, and advancing data quality, model performance, and governance across projects, with a hybrid in-office requirement in Chicago.

Qualifications

• Bachelor's degree in CS/Engineering or related field (or equivalent practical experience).
• 6+ years of software or ML engineering experience.
• Experience designing/operating production-grade ML systems at scale.
• Experience deploying ML/LLM-powered applications into production.

Responsibilities

• Define architecture and engineering standards for scalable ML platforms.
• Lead deployment of LLM-powered systems with open-source and commercial models.
• Set and monitor SLOs, reliability, and operational efficiency for ML platforms.
• Mentor engineers and raise engineering standards across teams.
• Translate business needs into scalable ML solutions and maintain data quality.

Skills

Python

SQL

MLOps

CI/CD

NLP

Transformers

Cloud infrastructure

Mentoring

English proficiency

Education

Bachelor's degree in CS/Engineering or related field

Advanced degree in ML/CS

Tools

Open-source ML frameworks

Job description

Bain & Company is hiring a Staff Engineer, Machine Learning to define architecture and operational excellence for scalable ML platforms. You will lead production deployment of LLM-powered systems and collaborate with cross-functional teams to deliver reliable, high-impact AI solutions.

The role emphasizes mentoring engineers, establishing CI/CD and MLOps practices, and advancing data quality, model performance, and governance across projects, with a hybrid in-office requirement in Chicago.