Machine Learning Architect

Role Overview

Tiger Analytics is seeking an Machine Learning Architect / Principal Data Scientist to design and deliver end-to-end machine learning solutions. This is a strategic and hands-on role focused on scalable ML system architecture, production deployment pipelines, and MLOps across multiple business verticals.

Responsibilities

• Design ML/AI system architecture across diverse business domains
• Lead ML system design discussions and make high-level decisions for:
  • model serving
  • data pipelines
  • MLOps frameworks
• Architect scalable, secure, cloud-native platforms for training, validation, deployment, and monitoring (AWS/GCP/Azure)
• Build reusable components and reference architectures for the ML lifecycle
• Define/enforce best practices for:
  • model versioning
  • ML CI/CD, testing, and rollback strategies
• Deploy and manage ML and data pipelines in production
• Work on containerization and orchestration for model deployment
• Collaborate with cross-functional teams (data scientists, engineers, product teams, stakeholders) in an Agile environment
• Implement MLOps best practices including version control, monitoring/alerting, and automated model deployment
• Communicate problem context effectively with a global/remote team

Requirements

• 10+ years of hands-on experience in data science/ML/MLOps
• Master’s or PhD in CS, electrical engineering, mathematics, or related field
• 4+ years of programming experience with Python
• 7+ years productionizing, monitoring, and maintaining ML models
• Strong Python and ML libraries experience: scikit-learn, TensorFlow, PyTorch
• Deep MLOps experience with tools such as MLflow, Kubeflow, Airflow, SageMaker, or Vertex AI
• Hands-on ML system design using AWS/Azure/GCP
• Strong understanding of data engineering, APIs, CI/CD, and model observability
• Excellent communication and stakeholder management

Benefits

• Career development in a fast-growing, entrepreneurial environment with meaningful individual responsibility.