AI Platform Engineer

The AI Platform Engineer builds and operates the machine learning and generative AI platform used by teams across Abbott Cancer Diagnostics. You'll own the full model lifecycle in production — data and feature pipelines, training and experimentation, evaluation and promotion, serving, and monitoring — along with the platform services, compute and tooling underneath it. This is hands-on infrastructure work backed by solid platform engineering practice: making inference fast and cheap, making the path from experiment to production repeatable and auditable, and shipping interfaces other engineers can build on — in support of software that ultimately reaches patients.

• Bachelor's degree in Computer Science, Engineering, AI/ML, or a related field; or equivalent practical experience.

• 3+ years building and operating production software, including significant work on ML or AI infrastructure.

• Strong Python, including software engineering fundamentals — automated testing, code review, and designing code others will read and extend.

• Experience with the ML model lifecycle: pipelines that carry a model from training through evaluation, deployment, monitoring, and retraining.

• Kubernetes experience — deploying, scaling, and debugging containerized workloads on a major cloud provider (AWS preferred).

• Experience with CI/CD, GitOps-based delivery, and infrastructure automation.

• ML workflow orchestration and experiment tracking (for example Kubeflow, Argo Workflows, Airflow, MLflow, or Weights & Biases).

• GPU infrastructure at scale: scheduling, multi-tenancy and resource isolation, autoscaling, and inference optimization such as quantization, batching, or graph compilation (for example ONNX Runtime or TensorRT).

• Feature stores, data versioning, or dataset lineage tooling.

• Progressive delivery for models — canary, shadow, or A/B deployment and automated rollback.

• Model monitoring and drift detection, including automated retraining triggers.

• Model governance and reproducibility practices: lineage tracking, audit trails, and approval workflows.

• Kubernetes-native serverless and event-driven autoscaling (for example Knative or KEDA).

• Experience building APIs or shared libraries consumed by other engineering teams.

• Production experience in an additional systems language such as Go or Java.

• Fine-tuning foundation models, or building distributed training pipelines.

• Experience delivering software in a regulated environment (HIPAA, CLIA, GxP, SOC 2).

We are an equal employment opportunity employer. All qualified applicants will receive consideration for employment without regard to age, color, creed, disability, gender identity, national origin, protected veteran status, race, religion, sex, sexual orientation, and any other status protected by applicable local, state, or federal law. Applicable portions of the Company’s affirmative action program are available to any applicant or employee for inspection upon request.

In specific locations, the pay range may vary from the range posted.