Machine Learning Engineer

This inclusive employer is a member of myGwork - the largest global platform for the LGBTQ+ business community.

Position Type : Full time

Type Of Hire : Experienced (relevant combo of work and education)

Education Desired : Bachelor's Degree

Travel Percentage : 1 - 5%



FIS technology processes more than $40 Trillion per year and enables 95% of the world's leading banks. Our Fraud Intelligence team is on the cutting edge of data science and machine learning technology that detects and prevents fraud on a global scale. As a Machine Learning Data Engineer, you will tackle challenges ranging from identity theft to credit card fraud, to money laundering, and more. The technology you build will protect individuals, businesses and financial institutions from fraudsters ranging from individuals up to multinational organized crime rings. The fraud prevention space is fast-paced and rapidly changing. You will work cross-discipline with data scientists, analytics, product, and more. Our ideal candidate not only brings technical skills to the table but has the appetite to dig into deeply complex problems, while learning new skills along the way. We are leading the way and leveraging our wealth of data to create best-in-class solutions.

We are looking for a 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

What you will need

Added bonus if you have

Translate product requirements into clean, maintainable, scalable, and well documented code.
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.
Monitor and optimize machine learning models in production.
Stay up to date with the latest developments in machine learning and data management.
Debug and troubleshoot software issues to ensure business continuity, and a high bar for end-user experience.
Participate in code reviews to ensure code quality, maintainability, and adherence to coding standards.
Write unit & integration tests, implement observability, and comply with best practices for builds and deployment to ensure the quality and reliability of our platform.
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.
Estimate project timelines, ensuring that projects you own stay on track and escalating as needed.
At a minimum, a Bachelor's in CS or equivalent education, or equivalent work experience.
Strong problem solving and analytical skills.
Solid computer science fundamentals including data structures, algorithms, design patterns, and performance optimization.
Excellent communication and cross-functional collaboration skills to thrive in a fast-paced environment.
Experience with data management, data, and build pipelines.
Experience with building and deploying machine learning models.
Experience with AWS, Snowflake, Databricks or similar technologies.
Typical qualifications for the role are 2+ years of relevant professional experience or equivalent advanced education.
Experience with financial services data sources.
Experience with MLflow and Feast or other Feature Stores is helpful.
Proficiency in modern development frameworks and languages. (e.g., Java, Python, Go).
Experience with version control systems (Git).
Experience with DevOps practices like continuous integration and continuous delivery (CI/CD).
Effective communication and collaboration skills, and a history of collaborating effectively with your team and cross-functional stakeholders.
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