Applied Production Machine Learning Engiineer

Fiserv • Alpharetta, GA

6d • Remote

About The Position

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.

At Fiserv, you build production machine learning software that powers real-time decisioning at scale across payments and financial services. You turn model concepts into dependable services and data pipelines with strong performance, observability, risk controls, and operational discipline. You partner with product, data science, and platform engineering teams to improve decisions, reduce losses, accelerate operations, and enhance client experience.

Requirements

• 9+ years of experience in machine learning and data science.
• 9+ years of experience deploying AI solutions in cloud environments.
• 7+ years of experience leading large-scale projects in a matrixed environment across project delivery, consulting, or engineering.
• Bachelor’s degree in computer science, data science, or a related field.

Nice To Haves

• 8+ years of experience in the financial services industry.
• 7+ years of experience with Python, Java, or Scala, including hands-on use of ML frameworks such as TensorFlow, Keras, or Scikit-learn.
• 6+ years of experience with SQL and NoSQL databases such as PostgreSQL, MongoDB, or Cassandra.

Responsibilities

• Architect, design, and implement scalable machine learning models and real-time inference services for high-throughput, low-latency decisioning in production financial workflows.
• Build and optimize data ingestion, feature, and API-driven services using Python, Java, or Scala with Spark, Kafka, Airflow, Snowflake, and Databricks to support reliable real-time decisions.
• Strengthen production reliability by monitoring model and data drift, enforcing data pipeline discipline, and applying risk controls across deployed decisioning systems.
• Partner across product, data science, and engineering teams to translate complex business requirements into technical specifications, deployable ML solutions, and dependable automated decisioning systems.
• Conduct structured experimentation with TensorFlow and PyTorch, and deploy and operate ML applications using Docker and Kubernetes in cloud environments with release automation, monitoring, and continuous optimization.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior