Note: The job is a remote job and is open to candidates in USA. eCom Solutions Inc is a company focused on ML platform engineering and MLOps. They are seeking an ML Engineer to enable algorithm development, model packaging, and deployment workflows across imaging datasets.
Responsibilities
- 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
Skills
- 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
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Biomedical Engineering, or related field
- 4+ years of experience in ML engineering, MLOps, or applied AI platform development
- 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
Company Overview
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