Senior ML Engineer

Momentive Software•Atlanta, GA

11h•Remote

About The Position

We are hiring a Senior ML Engineer to build, deploy, and support machine learning models and optimization systems on AWS. This role sits between data science and platform engineering, turning models into reliable production services and batch pipelines. The ideal candidate has deep experience with MLOps, APIs, cloud-native deployment, model serving, workflow orchestration, and enterprise-scale ML platforms.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
7+ years in software engineering or platform engineering, with 3+ years in ML engineering or MLOps.
Strong experience deploying ML models to production on AWS.
Hands-on experience with AWS services such as SageMaker, ECS, EKS, Lambda, Athena, S3, SQS, SNS, and CDK/CloudFormation/Terraform.
Strong Python skills and working knowledge of Java, Scala, or Go.
Experience with ML frameworks and tools such as scikit-learn, TensorFlow, XGBoost, FastAPI, Airflow, Spark, and Jupyter.
Experience building REST APIs around models for enterprise consumption.
Experience with Kubernetes, CI/CD pipelines, infrastructure as code, and monitoring.
Strong background in distributed systems, microservices, and cloud-native architecture.

Nice To Haves

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.

Responsibilities

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.