Role overview
Momentive Software amplifies the impact of over 20,000 purpose-driven organizations in over 30 countries, with over $11 billion raised and 55 million members served to date. Mission-driven nonprofits and associations rely on Momentive’s cloud-based software and services to address their most pressing challenges – from engaging their communities to simplifying operations and growing revenue. Designed to help organizations connect more, manage more, and ultimately expect more, Momentive's solutions are built with reliability at the core and strategically focus on fundraising, learning, events, careers, volunteering, accounting, and association management. Momentive partners with organizations that believe "good enough" is never enough – so they can bring on better outcomes for everyone they serve. Learn more at momentivesoftware.com .
What you'll work on
Productionize machine learning and optimization models as scalable APIs, batch jobs, and event-driven services.
Build and maintain ML deployment pipelines on AWS using container, serverless, and Kubernetes-based patterns.
Partner with data scientists to integrate models into enterprise applications and operational systems.
Design model inference services, batch scoring pipelines, and orchestration layers for real-time and offline use cases.
Implement model monitoring, validation, drift detection, and post-deployment support processes.
Build robust data ingestion and feature pipelines using streaming and batch architectures.
Create reusable ML service frameworks, deployment templates, and CI/CD workflows.
Improve model reliability, latency, and cost efficiency in production.
Support experimentation, tooling evaluation, and platform decisions for ML lifecycle management.
What we're looking for
Experience serving models in real-time and batch settings at enterprise scale.
Experience with NLP, optimization models, forecasting, classification, or fraud and identity models.
Experience with Red Hat OpenShift or Kubernetes-based ML deployments.
Experience with data lakes, Spark, Parquet, streaming ingestion, and large-scale analytics platforms.
Exposure to multi-cloud environments including AWS, GCP, and Azure.
Ability to mentor engineers and drive architecture standards.