Senior Machine Learning Engineer

Allied Benefit Systems•Chicago, IL

1d•Remote

About The Position

The Senior Machine Learning Engineer is responsible for designing, building, and deploying scalable machine learning systems that drive business impact. This role will partner closely with data scientists, AI Technical Product Owners, and engineering teams to integrate machine learning capabilities into real business processes. The emphasis is on operational excellence, scalability, and long-term maintainability rather than research and experimentation.

Requirements

Bachelor’s degree in Computer Science, Math, Statistics, or equivalent work experience required.
6+ years of strong experience building and operating machine learning systems in production environments.
Solid software engineering skills with Python and familiarity with modern ML frameworks such as PyTorch or TensorFlow.
Experience with data pipelines, workflow orchestration, and model deployment patterns.
Hands on experience with cloud platforms and managed ML services, with Azure, AWS, and/or Databricks experience preferred.
Understanding of MLOps concepts including model versioning, monitoring, testing, and lifecycle management.
Ability to work cross functionally and translate between data science, engineering, and business stakeholders.

Nice To Haves

Experience working with sensitive data in regulated industries such as healthcare or insurance is strongly preferred.

Responsibilities

Design and implement end to end machine learning pipelines that support data ingestion, feature generation, model training, validation, deployment, and monitoring.
Operationalize models in coordination with data scientists and ensure they run reliably with requisite alerts and monitoring in production environments.
Build reusable frameworks and patterns that reduce friction when deploying new models or updating existing ones.
Ensure pipelines are secure, auditable, and appropriate for use in regulated enterprise environments.
Own and evolve the MLOps toolchain that supports model versioning, artifact management, experiment tracking, and deployment workflows.
Implement continuous integration and deployment practices for machine learning systems.
Establish monitoring and alerting for model performance, data quality, drift, and system health.
Partner with cloud and platform teams to manage compute resources, cost controls, and environment configurations.
Work with application engineering teams to integrate machine learning outputs into downstream systems and user workflows.
Support real time and batch inference patterns depending on business needs.
Ensure that machine learning services meet performance, reliability, and availability expectations for production use.
Collaborate closely with data scientists to shape models that are production ready and operationally sustainable.
Provide guidance on feature engineering, model packaging, and performance tradeoffs from a deployment perspective.
Document standards, patterns, and best practices for building and operating machine learning systems.
Contribute to the maturation of the organization’s overall AI and ML engineering discipline.
Other duties as assigned