Software Engineer Senior - Machine Learning

at  FIS Global

JOB DESCRIPTION

As the world works and lives faster, FIS is leading the way. Our fintech solutions touch nearly every market, company and person on the planet. Our teams are inclusive and diverse. Our colleagues work together and celebrate together. If you want to advance the world of fintech, we’d like to ask you: Are you FIS?

EEOC STATEMENT

FIS is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics. The EEO is the Law poster is available here supplement document available here
For positions located in the US, the following conditions apply. If you are made a conditional offer of employment, you will be required to undergo a drug test. ADA Disclaimer: In developing this job description care was taken to include all competencies needed to successfully perform in this position. However, for Americans with Disabilities Act (ADA) purposes, the essential functions of the job may or may not have been described for purposes of ADA reasonable accommodation. All reasonable accommodation requests will be reviewed and evaluated on a case-by-case basis.

ABOUT THE ROLE:

We are looking for a Senior Machine Learning Engineer to help us build a brand-new financial technology platform for the future. We look for people who operate like owners, who loves to learn, have grit, and operate with integrity and empathy. You’re encouraged to apply even if your experience doesn’t precisely match the job description. Your skills and passion will stand out — and set you apart. We welcome diverse perspectives and people who are not afraid to challenge assumptions.

WHAT YOU WILL BE DOING:

• Understand business objectives, product requirements and develop ML algorithms that achieve them.
• Build Prototypes, and proof of concepts to determine feasibility, then drive data-based decisions.
• Run experiments to assess performance and improvements.
• Provide ideas and alternatives to drive a product/feature.
• Define data and feature validation strategies.
• Deploy models to production systems and operate them including monitoring and troubleshooting
• Design, build, and manage the data pipelines and infrastructure that collect, store, and process large volumes of transactional and customer data from various sources.
• Develop, deploy, and scale machine learning models and applications in production and lower environments
• Ensure data quality, security and availability for the data, notebooks, models, experiments, and applications.
• Integrate ML models with the SaaS platform and other services and tools, such as the model registry, feature store, data lake, and event streams.
• Collaborate with data scientists to develop and test machine learning models.
• Drive code reviews to ensure code quality, maintainability, and adherence to coding standards.
• Provide live on-call support by participating in the team on-call rotation and owning production issues from root cause analysis to resolution to future prevention.
• Partner with cross-functional teams (engineering, product, design, security, compliance etc.) to bring ideas to life
• Build secure, robust, scalable, and performant systems for processing transactions and managing customer data.