About the role

Role Overview

As an ML Engineer, you will build and support ML/MLOps capabilities for a Global Imaging Platform in healthcare/life sciences.

Responsibilities

Build and support ML/MLOps capabilities for the Global Imaging Platform
Integrate ML models and workflows with platform components (e.g., IDPs, ARDS, App Catalog, model registry)
Use Amazon SageMaker as the primary environment for algorithm development
Package algorithms for repeatable execution, including:
- versioning
- lineage
- auditability
Implement model metadata, version control, and experiment tracking with reproducible build patterns
Support in-platform inferencing workflows for SageMaker-hosted/connected models

Requirements

Strong hands-on experience with Python and ML engineering libraries
Experience with AWS SageMaker and model deployment, including endpoint/inference workflows
Solid MLOps knowledge: model registry, experiment tracking, versioning, and reproducibility
Familiarity with CI/CD for ML model packaging, deployment, and promotion
Familiarity with GxP-ready workflows and validation evidence generation

Nice to Haves

Collaboration experience with architects, data scientists, and subject matter experts (implied)

What’s On Offer

Opportunity to work on a global imaging platform with significant impact in healthcare/life sciences
Collaboration with cross-functional teams to advance cutting-edge ML engineering

About Haystack

Haystack is a company involved in connecting talent with opportunities, including roles focused on building advanced machine learning and MLOps capabilities. The posting highlights an enterprise client working on healthcare imaging innovation and a global imaging platform. The work centers on applying ML engineering to life sciences and healthcare platforms.

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