ML Platform Engineer - Production AI & ML Infra

Abbott; Madison (WI); On-site; USD 61,000 - 123,000; Full time

Job summary

Abbott in Madison, WI seeks an AI Platform Engineer to build and operate the ML and generative AI platform used across Abbott Cancer Diagnostics. You will own the full model lifecycle in production—from data pipelines through training, evaluation, serving, and monitoring.

This hands-on role emphasizes fast, cost-efficient inference and repeatable deployment. You will design APIs, tooling, and interfaces for other engineers, with strong emphasis on governance and reliability.

Qualifications

• Bachelor's degree in Computer Science, Engineering, AI/ML, or related field or equivalent practical experience.
• 3+ years building and operating production software, including significant ML/AI infrastructure work.
• Strong Python with software engineering fundamentals and testing.
• Experience with the ML model lifecycle from training to retraining.
• Kubernetes experience with cloud deployments (AWS preferred).
• Experience with CI/CD, GitOps, and infrastructure automation.

Responsibilities

• Build and maintain ML data, feature, and training pipelines for ML/LLM workloads with lineage.
• Implement automated evaluation and promotion gates for production-ready models.
• Automate the model lifecycle end-to-end via CI/CD and GitOps.
• Build and operate production model-serving infrastructure for LLMs and predictive models.
• Architect and manage GPU infrastructure for training and inference workloads.
• Instrument platform and models with logging, telemetry, and cost tracking.

Skills

Python

Machine Learning Infrastructure

Kubernetes

CI/CD

GitOps

AWS

Monitoring

Data Pipelines

LLMs

Education

Bachelor's degree in Computer Science or related field

Tools

Kubeflow

Argo Workflows

MLflow

Weights & Biases

ONNX Runtime

TensorRT

Job description

Abbott in Madison, WI seeks an AI Platform Engineer to build and operate the ML and generative AI platform used across Abbott Cancer Diagnostics. You will own the full model lifecycle in production—from data pipelines through training, evaluation, serving, and monitoring.

This hands-on role emphasizes fast, cost-efficient inference and repeatable deployment. You will design APIs, tooling, and interfaces for other engineers, with strong emphasis on governance and reliability.