Senior ML Architect: Scalable AI & MLOps Leader

Aimlroles; United States; A distancia; USD 180.000 - 240.000; Jornada completa

Descripción de la vacante

Tiger Analytics is seeking a Principal Data Scientist to lead end-to-end ML and AI initiatives in a fast-growing analytics consulting environment. The role combines strategic planning with hands-on design, building scalable ML pipelines and production-grade deployments across multiple business domains.

The ideal candidate has 10+ years of data science and MLOps experience, deep cloud and system design expertise, and excellent communication to align business goals with technical execution.

Formación

• Master's or doctoral degree in CS, EE, or mathematics.
• 10+ years in analytics with 4+ years in Python programming.
• 7+ years of productionizing models and ML systems.
• Strong Python and ML libraries knowledge (scikit-learn, TensorFlow, PyTorch).
• Deep experience with MLOps tools and cloud platforms (AWS/Azure/GCP).
• Excellent communication and stakeholder management.

Responsabilidades

• Design and define system architecture for ML/AI solutions across domains.
• Lead ML system design and high-level choices for model serving and pipelines.
• Architect secure, scalable cloud-native platforms for training, validation, deployment, monitoring.
• Build reusable components and reference architectures for ML lifecycle.
• Enforce CI/CD, model versioning, testing, and rollback strategies.
• Deploy and manage ML pipelines in production; iterate with cross-functional teams.
• Collaborate with data scientists, engineers, product teams, and business stakeholders.

Conocimientos

Python programming

Machine learning

MLOps

Cloud architecture

System design

Educación

Master's or PhD in CS/EE/Math

Herramientas

MLflow

Kubeflow

Airflow

SageMaker

Vertex AI

TensorFlow

PyTorch

Descripción del empleo

Tiger Analytics is seeking a Principal Data Scientist to lead end-to-end ML and AI initiatives in a fast-growing analytics consulting environment. The role combines strategic planning with hands-on design, building scalable ML pipelines and production-grade deployments across multiple business domains.

The ideal candidate has 10+ years of data science and MLOps experience, deep cloud and system design expertise, and excellent communication to align business goals with technical execution.