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# AI Platform Engineer

- **Company:** Abbott Laboratories
- **Location:** Madison, WI, United States
- **Experience:** Experienced
- **Salary:** $61,300.0 - $122,700.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Adobe InDesign, Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Automation of Tests, Code Review, Continuous Integration, Software Debugging, Monitoring of Systems, Python (Programming Language), Machine Learning, Management of Software Versions, AI Infrastructure, Data Logging, Feature Store, Large Language Models, Discretization, Containerization, AI Platforms, Kubernetes, Infrastructure Automation Frameworks, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, TensorRT, Hardware Infrastructure, Drift Detection, Serverless Computing, GXP
- **Published:** October 16, 2026
- **Apply:** https://www.juju.com/job/1_ScrapingHub21935-ABLAUS31160332ENUSEXTERNAL

## About the Role

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). _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.

## Description

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. +