À propos
Role Overview We are seeking hands-on
Machine Learning Engineers
for an urgent staff augmentation engagement ( 6-month temporary role, with a possibility of extension ). In this role, you will support, fix, and optimize a high-traffic, global consumer platform. Operating under the direct guidance of a Staff ML Architect, you will focus heavily on daily MLOps execution, pipeline maintenance, and ensuring models perform reliably in a production-grade environment.
Key Responsibilities
Model Lifecycle Management:
Design, deploy, and monitor machine learning models within high-traffic environments, ensuring maximum reliability, performance, and scalability.
Pipeline Execution:
Maintain, troubleshoot, and optimize end-to-end ML pipelines. Integrate tools like Spark and Airflow to streamline the flow from raw data ingestion through to offline and online model evaluation.
Daily MLOps:
Execute daily model training and inference tasks. Build and manage automated containerized deployments to ensure smooth, continuous support for a major production platform.
Required Qualifications
Experience:
1–3 years of hands‑on experience in
Machine Learning, MLOps, or Data Science
within a professional environment.
GCP Expertise:
Solid experience with the Google Cloud Platform ecosystem, particularly
Vertex AI
components (Workbench, Pipelines, Model Registry).
ML Frameworks & DevOps:
Proficiency in modern ML frameworks (e.g.,
PyTorch ) and containerization tools ( Docker ) for automated builds.
Data Orchestration:
Practical experience managing data processing flows using
Apache Spark
and
Airflow .
Preferred Skills
Familiarity with real-time model serving and infrastructure (e.g.,
Triton Inference Server ,
Terraform ).
Previous experience in high-traffic, production‑grade environments.
Position Type 6 month temporary employee
Location Remote (Candidates must be based in the
US or Canada )
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Compétences linguistiques
- English
Avis aux utilisateurs
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