Principal / Staff Machine Learning Engineer

Fiserv•Berkeley Heights, NJ

8d•Onsite

About The Position

Calling all innovators - find your future at Fiserv. We're Fiserv, a global leader in Fintech and payments, and we move money and information in a way that moves the world. We connect financial institutions, corporations, merchants and consumers to one another millions of times a day - quickly, reliably, and securely. Any time you swipe your credit card, pay through a mobile app, or withdraw money from the bank, we're involved. If you want to make an impact on a global scale, come make a difference at Fiserv. Job Title Principal / Staff Machine Learning Engineer About your role: As a Machine Learning Engineer, you will design, build, and deploy scalable machine learning solutions that support credit risk and fraud management for Global Business Solutions (merchant business). You will collaborate closely with data scientists, engineers, and business stakeholders to translate complex use cases into resilient ML pipelines and decision frameworks. Your work will directly reduce risk of loss and enable revenue growth for our clients and Fiserv. What you'll do: Architect and maintain the machine learning deployment framework used to operationalize models and decisioning engines. Partner with stakeholders to define business use cases, success criteria, and project timelines, and own requirements gathering, solution design, deployment strategy, and performance tracking for data science deployments. Design, build, and manage batch and real-time machine learning pipelines to support deployment of models, decision frameworks, and optimization engines. Collaborate with Data and Decision Science team members to build feature libraries, feature stores, feedback loops, and reusable components that streamline model development, validation, and deployment. Own operational governance of the model lifecycle, including model versioning, promotion, rollback, deprecation, and ongoing monitoring for production machine learning systems. Develop and optimize cloud-based data architectures for scalability, low latency, reliability, and cost efficiency, implementing robust data quality controls, validation checks, test coverage, monitoring, and alerting for production-grade pipelines and data products. Deploy data and analytics solutions to Amazon Web Services (AWS) using CI/CD automation and DevOps best practices, and maintain clear technical documentation while collaborating within Agile workflows using tools such as JIRA and Confluence. Conduct analytics and support development of machine learning and predictive model pipelines and frameworks that advance credit risk and fraud strategies. Responsibilities listed are not intended to be all-inclusive and may be modified as necessary.

Requirements

6 years of experience in machine learning engineering, data engineering, or data science, designing and building production-grade data or ML pipelines in a commercial environment.
6 years of experience developing and optimizing data solutions on cloud platforms, including hands-on experience with AWS capabilities and Snowflake in an engineering context, along with production use of machine learning pipelines, feature stores, or MLOps practices.
6 years of experience creating Python microservices, working with containers, and developing application programming interfaces (APIs) to support data and machine learning solutions.
6 years of experience collaborating with internal and external stakeholders and driving initiatives using clear, effective verbal and written communication skills.
6 years of experience building scalable, low-latency systems or feature components that support analytics, machine learning, or decisioning use cases.
6 years of experience with CI/CD automation and DevOps practices to deploy, monitor, and support data solutions in production environments.
Bachelor’s degree or higher in Mathematics, Statistics, Computer Science, Engineering, or a related quantitative field, or equivalent combination of education, related experience and/or military experience.

Nice To Haves

Experience in machine learning operations.
Experience working in Agile delivery environments using tools such as JIRA and Confluence or similar work-tracking and collaboration platforms.
Experience in financial technology, payments, merchant acquiring, or lending domains, especially in credit risk or fraud management.
Experience with infrastructure-as-code tooling (such as Terraform or CloudFormation) and container orchestration platforms (such as Kubernetes or Amazon Elastic Kubernetes Service).

Responsibilities

Architect and maintain the machine learning deployment framework used to operationalize models and decisioning engines.
Partner with stakeholders to define business use cases, success criteria, and project timelines, and own requirements gathering, solution design, deployment strategy, and performance tracking for data science deployments.
Design, build, and manage batch and real-time machine learning pipelines to support deployment of models, decision frameworks, and optimization engines.
Collaborate with Data and Decision Science team members to build feature libraries, feature stores, feedback loops, and reusable components that streamline model development, validation, and deployment.
Own operational governance of the model lifecycle, including model versioning, promotion, rollback, deprecation, and ongoing monitoring for production machine learning systems.
Develop and optimize cloud-based data architectures for scalability, low latency, reliability, and cost efficiency, implementing robust data quality controls, validation checks, test coverage, monitoring, and alerting for production-grade pipelines and data products.
Deploy data and analytics solutions to Amazon Web Services (AWS) using CI/CD automation and DevOps best practices, and maintain clear technical documentation while collaborating within Agile workflows using tools such as JIRA and Confluence.
Conduct analytics and support development of machine learning and predictive model pipelines and frameworks that advance credit risk and fraud strategies.

Benefits

Fuel Your Life program to support your physical, financial, social, and emotional well-being.
Paid holidays and generous time away policies.
No-cost mental health support through Employee Assistance Programs.
Living Proof program to recognize your peers’ extra effort with points redeemable for rewards.
Eight Employee Resource Groups to foster a collaborative culture and expand your network.
Unparalleled professional growth with training, development, and internal mobility opportunities.
Medical, dental, vision, life, and disability insurance options available from day one.
Retirement planning and discounted shares with the Employee Stock Purchase Plan.
Tuition assistance and reimbursement program.
Paid parental, caregiver, and military leave.