Local only ll AI/ML Engineer – ML Architecture & MLOps ll San Francisco, CA ll 6
14+ Year experience only
Job Title: AI/ML Engineer with Strong ML Architecture.
Location: San Francisco, CA
The ideal candidate is an AI/ML Engineer with strong hands-on ML expertise and architecture-level capabilities, who can design scalable ML solutions while also implementing and operationalizing them in production environments.
Role Overview
We are seeking a highly experienced AI/ML Engineer with strong machine learning expertise and architecture-level experience to support the design, development, and implementation of scalable ML platform capabilities within the Global Imaging Platform (GIP).
The ideal candidate should have a strong understanding of machine learning algorithms, ML system architecture, MLOps, model lifecycle management, and enterprise-scale ML platform integration, along with hands-on experience in Python and AWS SageMaker.
This role focuses on enabling algorithm development, model packaging and registration, reproducible execution, and governed deployment workflows across imaging datasets, Integrated Data Products (IDPs), and Analysis-Ready Datasets (ARDs).
The primary emphasis is on ML architecture, platform engineering, MLOps, model integration, and validation readiness, rather than developing new clinical interpretation algorithms.
Required Technical Skills
ML Architecture: Strong understanding of machine learning system architecture, scalable ML pipelines, model integration, and production ML systems.
Python: Strong hands-on programming experience with Python and relevant ML engineering libraries.
Machine Learning: Solid understanding of ML algorithms, model training, evaluation, inference, and deployment.
AWS SageMaker: Experience with model deployment, endpoint configuration, inference workflows, and ML lifecycle management.
MLOps: Experience with model registries, experiment tracking, model versioning, reproducibility, and automated ML workflows.
ML Frameworks: Practical experience with PyTorch, TensorFlow, scikit-learn, or similar frameworks.
Containerization and Orchestration: Familiarity with Docker, Kubernetes, Amazon EKS, and related technologies.
CI/CD and Version Control: Experience with GitHub, automated testing, build pipelines, deployment automation, and release management.
Data Engineering and Integration: Experience with data pipelines, APIs, metadata management, and data/model lineage.
Production Readiness: Understanding of model packaging, environment reproducibility, rollback strategies, monitoring, and lifecycle controls.
Documentation: Ability to prepare technical architecture documents, implementation designs, and validation evidence.
Preferred Skills
Experience in life sciences, clinical imaging, healthcare AI, or regulated data platforms.
Familiarity with medical imaging formats and workflows, including DICOM, radiology, ophthalmology, or digital pathology.
Exposure to GxP, Software as a Medical Device (SaMD), computer system validation, audit trails, traceability, and 21 CFR Part 11-aligned environments.
Experience integrating ML models into enterprise platforms, application catalogs, or centralized model registries.
Experience with cloud-native architecture, observability dashboards, and production ML monitoring.
Familiarity with Google Vertex AI, Posit Workbench, or HPC-based model development environments.
Expected Deliverables
ML architecture and model integration patterns for the Global Imaging Platform.
ML model packaging, registration, and deployment workflows.
AWS SageMaker integration support for the GIP algorithm framework.
Reproducible ML execution and inference patterns.
Model metadata, lineage, versioning, and experiment-tracking implementation.
CI/CD workflows supporting model deployment, promotion, and rollback.
ML lifecycle documentation and technical implementation notes.
Support for validation evidence, governance, and audit-readiness documentation.
Model monitoring, observability, and lifecycle management capabilities.
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