AI Platform Engineer

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

Job summary

Abbott is seeking an AI Platform Engineer to own the full model lifecycle from data pipelines to production serving for cancer diagnostics. You will build and operate the ML platform that enables teams to train, evaluate, deploy, and monitor models with low latency and scalable GPU infrastructure.

You will implement automated evaluation gates, promote models through CI/CD, and extend registries and APIs for self-service platform usage.

Qualifications

• Bachelor's degree or equivalent practical experience.
• 3+ years building and operating production software including ML/AI infrastructure.
• Strong Python, including software engineering fundamentals—testing, code review, and readable code.
• Experience with the ML model lifecycle: pipelines from training through evaluation, deployment, monitoring, and retraining.
• Kubernetes experience—deploying, scaling, and debugging containerized workloads on AWS or major cloud provider.
• Experience with CI/CD, GitOps-based delivery, and infrastructure automation.

Responsibilities

• 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 for production.
• Automate the model lifecycle end to end via 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 formats/runtimes.
• Architect and manage GPU infrastructure—scheduling, autoscaling, resource isolation, and utilization efficiency for training and inference.

Skills

Python programming

CI/CD practices

Go/Java

Kubernetes experience

Education

Bachelor's degree in Computer Science, Engineering, AI/ML, or related field

Tools

Kubeflow

Argo Workflows

Airflow

MLflow

Weights & Biases

ONNX Runtime

TensorRT

Knative

KEDA

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 122,000 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. 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

• 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).

Other Information

• 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.
• 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
• Medical Surveillance: No
• Significant Work Activities: 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: http://webstorage.abbott.com/common/External/EEO_English.pdf
• EEO is the Law link - Espanol: http://webstorage.abbott.com/common/External/EEO_Spanish.pdf
• 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. The people of Abbott come to work each day with relentless energy, enthusiasm and a promise to enhance the health and well‑being of millions of people. They push the boundaries to help manage and treat some of life’s greatest health challenges. We invite you to explore opportunities at Abbott, to see if your talents and career aspirations may fit with our openings. An equal opportunity employer, Abbott welcomes and encourages diversity in our workforce.