AI Platform Engineer

Abbott Laboratories

Madison (WI)

On-site

USD 61,000 - 123,000

Full time

4 days ago

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Job summary

Abbott is seeking an AI Platform Engineer to build and operate the ML and generative AI platform used across Cancer Diagnostics. You will own the full model lifecycle in production—from data pipelines to serving and monitoring—while delivering robust, auditable platform services.

You will implement automated evaluation gates, enable CI/CD and GitOps pipelines, and scale GPU infrastructure for training and inference.

Qualifications

Bachelor's degree in Computer Science, Engineering, AI/ML, or related field.
3+ years building and operating production software, incl. ML/AI infra.
Strong Python and software engineering fundamentals (testing, code reviews).
Experience with ML model lifecycle: training, evaluation, deployment, monitoring, retraining.
Kubernetes experience—deploying, scaling, debugging containerized workloads on a major cloud provider (AWS preferred).
Experience with CI/CD, GitOps-based delivery, and infrastructure automation.

Responsibilities

Build and maintain data, feature, and training pipelines for ML/LLM workloads.
Implement automated evaluation and promotion gates for models.
Automate the model lifecycle end-to-end via CI/CD and GitOps.
Operate production model-serving infra with low latency and autoscaling.

Skills

Python

Kubernetes

CI/CD

GitOps

ML Platform

Education

Bachelor's degree in CS/Engineering/AI/ML

Tools

Kubeflow

Argo Workflows

Airflow

MLflow

Weights & Biases

Job description

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 115,000 colleagues serve people in more than 160 countries.

Abbott is about the power of health. For more than 135 years, Abbott has been helping people reach their potential — because better health allows people and communities to achieve more. With a diverse, global network serving customers in more than 160 countries, we create new solutions — across the spectrum of health, around the world, for all stages of life. Whether it’s next-generation diagnostics, life-changing devices, science-based nutrition, or novel reformulations, we are advancing some of the most innovative and revolutionary technologies in healthcare, helping people live their best lives through better health.

JOB DESCRIPTION:

Position Overview

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.

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

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.

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).

Equal Employment Opportunity

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.

Abbott is an Equal Opportunity Employer of Minorities/Women/Individuals with Disabilities/Protected Veterans. EEO is the Law link - English: http://webstorage.abbott.com/common/External/EEO_English.pdf EEO is the Law link - Espanol: http://webstorage.abbott.com/common/External/EEO_Spanish.pdf

Compensation

The base pay for this position is $61,300.00 – $122,700.00. In specific locations, the pay range may vary from the range posted.

Job Family

Product Development

Division

ONCO Cancer Diagnostics

Location

United States > Madison : 5505 Endeavor Ln

Work Shift

Standard

Travel

Yes, 10 % of the Time