ML Platform Engineer

Infinity Tech Group Inc

New York, United States

26 days ago

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

6 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Algorithm Design Audit Trail Cloud Engineering Continuous Integration Information Engineering Data Infrastructure Dicom Github Python (Programming Language) Machine Learning

+16 more

Metadata Release Management Tensorflow Azure Machine Learning Management of Software Versions IBM Watson Health Pytorch AI Platforms Kubernetes Information Technology Data Management Machine Learning Operations Software Version Control Data Pipelines GXP Docker

Job description

Build and support ML / MLOps capabilities within the Global Imaging Platform.
Integrate ML models and workflows with GIP IDPs, ARDs, App Catalog, and model registry.
Support SageMaker integration as the first connected algorithm development environment.
Package algorithms for repeatable execution, versioning, lineage, and auditability.
Implement model metadata, version control, experiment tracking, and reproducible build patterns.
Support in-platform inferencing workflows for SageMaker-hosted or connected models.
Work with data engineers, architects, and validation teams to ensure traceability from dataset to model output.
Support CI/CD pipelines for ML model packaging, deployment, rollback, and promotion.
Contribute to governance, observability, monitoring, and lifecycle controls for ML models.
Support validation evidence generation, including documentation for GxP-ready workflows where applicable.
Collaborate with architects, data scientists, platform engineers, and Genentech SMEs to align ML workflows with platform standards.
Assist with onboarding future connected environments such as Vertex AI, Posit Workbench, and HPC., * SageMaker integration support for GIP algorithm framework.
Reproducible ML execution patterns.
Model metadata, lineage, and versioning implementation.
ML lifecycle documentation and technical implementation notes.
Support for validation evidence and audit-readiness documentation.
Support for model deployment, monitoring, and rollback workflows.

Requirements

Strong hands-on experience with Python and ML engineering libraries.
Experience with AWS SageMaker, model deployment, and endpoint / inference workflows.
Experience with MLOps, model registry, experiment tracking, model versioning, and reproducibility.
Familiarity with tools such as MLflow, Docker, GitHub, CI/CD pipelines, Kubernetes / EKS, or similar.
Experience working with data pipelines, APIs, metadata, and lineage concepts.
Understanding of model packaging, release management, rollback, and environment reproducibility.
Ability to work with cross-functional teams across architecture, data engineering, validation, and product teams.
Strong documentation skills for technical designs, implementation notes, and validation evidence., * Experience in life sciences, clinical imaging, healthcare AI, or regulated data platforms.
Familiarity with imaging formats and workflows such as DICOM, radiology, ophthalmology, or digital pathology.
Experience with PyTorch, TensorFlow, or similar ML frameworks.
Exposure to GxP, SaMD, validation, audit trail, traceability, or 21 CFR Part 11-aligned environments.
Experience integrating ML models into enterprise platforms or application catalogs.
Experience with cloud-native architecture and observability dashboards., * Bachelor s or Master s degree in Computer Science, Data Science, Engineering, Biomedical Engineering, or related field.
6+ years of experience in ML engineering, MLOps, or applied AI platform development.
Prior experience supporting enterprise-scale ML platforms or regulated data environments is preferred. *