Senior ML Engineer: MLOps & Glassbox Interpretability Lead

Equifax, Inc.; Alpharetta (GA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

4 days in-office collaboration (Mon-TH

Friday Flexibility

Job summary

Equifax, Inc. is seeking a Senior ML Engineer in Alpharetta to build the MLOps foundation and an interpretability framework for credit risk engines.

You will bridge Mechanistic Interpretability (XAI) research with production workflows, designing systems that map latent embeddings to human-readable credit concepts and serving models at scale with transparency and regulatory defensibility.

Qualifications

• BS degree in a STEM field; Master’s degree preferred.
• 7+ years of related work experience with leadership of ML/DS/DevOps or related teams.
• Experience end-to-end development of ML models with scalability and reliability.

Responsibilities

• Design complex systems for training and running machine learning models with industry best practices.
• Define projects and scope for teams of engineers and guide their completion.
• Develop, identify, and report IP through patent applications, disclosures, white papers, and presentations.
• Communicate effectively with executives, customers, and cross-functional teams.
• Contribute to all phases of product development from analysis to deployment.
• Deliver on company initiatives and align projects with technical vision.
• Collaborate with product, architects, and data teams to leverage AI, ML, and data engineering.
• Participate in peer design and code reviews.
• Drive improvements and innovations to advance the IT organization.
• Mentor and elevate colleagues; may lead a team.

Skills

Machine learning

Team leadership

MLOps

Regulatory compliance

Interpretability

Education

Bachelor's in STEM

Master’s degree preferred

Tools

Python

TensorFlow/PyTorch

Job description

Equifax, Inc. is seeking a Senior ML Engineer in Alpharetta to build the MLOps foundation and an interpretability framework for credit risk engines.

You will bridge Mechanistic Interpretability (XAI) research with production workflows, designing systems that map latent embeddings to human-readable credit concepts and serving models at scale with transparency and regulatory defensibility.