AI Platform Engineer

Abbott is a global healthcare leader that helps people live more fully at all stages of life. Our portfolio of life-changing technologies spans the spectrum of healthcare with leading businesses and products in diagnostics medical devices nutritionals and branded generic medicines. Our 122000 colleagues serve people in more than 160 countries.

Position Overview

The AI Platform Engineer builds and operates the machine learning and generative AI platform used by teams across Abbott Cancer Diagnostics. Youll 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.

Essential Duties
Include but are not limited to the following:

• Build and maintain data feature and training pipelines for ML and LLM workloads ingestion transformation fine-tuning distributed training and reproducible experiment execution with lineage tracked from dataset and code to resulting model.

• Implement automated evaluation and promotion gates performance benchmarks regression checks and validation criteria that determine whether a model advances toward production.

• Automate the model lifecycle end to end through CI/CD and GitOps: packaging promotion across environments progressive rollout and rollback.

• Build and operate production model-serving infrastructure for LLMs and predictive models including inference optimization autoscaling and low-latency serving across multiple model formats and runtimes.

• Architect and manage GPU infrastructure scheduling autoscaling resource isolation and utilization efficiency for training and inference workloads.

• Instrument the platform and the models on it structured logging telemetry drift detection and cost tracking and build the triggers and pipelines that close the loop into retraining and revalidation.

• Extend model dataset and artifact registries metadata systems and versioning so every deployed model has a traceable auditable history.

• Build the developer-facing surface of the platform: APIs SDK components templates documentation and runbooks that make it self-service backed by well-tested code and active participation in design and code review.

• Uphold company mission and values through accountability innovation integrity quality and teamwork.

• Maintain regular and reliable attendance.

• Act with an inclusion mindset and model these behaviors for the organization.

Minimum Qualifications

• Bachelors 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.

Preferred Qualifications

• 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 Companys affirmative action program are available to any applicant or employee for inspection upon request.

The base pay for this position is

$61300.00 $122700.00

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

Product Development

ONCO Cancer Diagnostics

United States > Madison : 5505 Endeavor Ln

Standard

Yes 10 % of the Time

No

Continuous sitting for prolonged periods (more than 2 consecutive hours in an 8 hour day) Keyboard use (greater or equal to 50% of the workday)

Abbott is an Equal Opportunity Employer of Minorities/Women/Individuals with Disabilities/Protected Veterans.

EEO is the Law link - English: EEO is the Law link - Espanol: Experience:

IC