Senior Machine Learning Architect

Equifax, Inc.; Atlanta (GA); On-site; USD 120,000 - 180,000; Full time

Job summary

Equifax is seeking a Machine Learning Engineer to design complex systems for training and running ML models, applying best practices to ensure scalability, reliability, and performance. You will work with product teams, architects, and executives to define direction, set milestones, review designs, and deliver end-to-end solutions.

The ideal candidate has a BS in STEM (MS preferred), 7+ years of experience, proven leadership of ML/DS teams, and hands-on expertise in model development,

Qualifications

• BS degree in a STEM field; MS preferred.
• 7+ years of related work experience, including leading ML/DS teams.

Responsibilities

• Design complex systems for training and running ML models with best practices for scalability, reliability, and performance.
• Define projects and scope for teams of engineers and guide their completion.
• Develop, identify, and report IP through papers and presentations; potential for patents.
• Collaborate with product teams, architects, and executives to define technical direction and milestones.
• Lead end-to-end ML deployment from ideation to production and monitoring.

Skills

Team Leadership

ML Systems

End-to-End ML

Cloud Certification

Education

Bachelor's degree in STEM

Master's degree preferred

Tools

Cloud Certification

Job description

Equifax is excited to add a Machine Learning Engineer to our team.

What you'll do

• Design complex systems of systems for training and running machine learning models with industry best practice
• Define projects and scope for teams of engineers and guide their completion
• Develop, identify, and report intellectual property through patent applications, invention disclosures, white papers, and presentations
• Demonstrate effective, respectful, and honest communication when collaborating with colleagues including executives, customers, and peers from other businesses and institutions
• Contribute to all phases of product development and delivery from Analysis & Design all the way through to successful Deployment
• Deliver on company initiatives and prioritize projects supporting your long term technical vision
• Collaborate with the product team, architects, and others to understand the opportunities and limitations of AI, ML, and data engineering
• Participate in peer design and code reviews
• Show initiative to identify and drive forward improvements and innovations that add value and move the IT organization forward
• Elevate the performance of colleagues through training, mentoring, and promoting best practices; may function as a team lead

What experience you need

• BS degree in a STEM major or equivalent job experience required; Master's Degree preferred; AI/ML coursework preferred
• 7+ years of related work experience, including proven experience leading a team of MLE, DS, SDE, DevOps, or related roles
• Experience with end-to-end development of ML models, from ideation to deployment, ensuring best practices, scalability and reliability
• Cloud Certification Strongly Preferred

What could set you apart

• Application Development/Programming - Ability to review code for quality, performance, and efficiency, and optimize critical parts of the codebase; Ability to establish the best practices of Software Development Life Cycle for the team
• Artificial Intelligence - Designing scalable and maintainable machine learning architectures and frameworks for the organization's products and services; Ability to define the technical vision and roadmap for the MLE team aligned with the organization's goals and industry trends
• Big Data Analytics - Deep understanding of the domain or industry in which the machine learning solutions are being applied, enabling the company to develop impactful big data solutions
• Cloud Computing - Proficiency in data architecture design, data strategy development, data orchestration, data integration, ETL development, data modeling, parallel processing and performance optimization.
• Collaboration - Being able to engage with internal stakeholders, including data scientists, business leaders, product managers, and executives, to understand requirements and present technical solutions; Ability to collaborate with other teams, such as software engineering, data engineering, and business intelligence, to integrate machine learning solutions into larger systems.
• Mathematics - Ability to read and comprehend research papers in latest machine learning field, and applying innovative techniques to real-world problems
• Technical Leadership - Be able to lead and manage a team of machine learning engineers, data scientists, or related roles. Ability to set clear goals, provide guidance, and foster a collaborative and productive team environment