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Machine Learning Architect

3 weeks, 6 days ago
United States
Full-time
Lead
Machine Learning Engineer
DevOps and Infrastructure
Agile Apache Airflow AWS Azure CI/CD GCP Kubeflow Machine Learning MLflow MLOps Python PyTorch SageMaker Scikit-learn System Design TensorFlow Vertex AI
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Tiger Analytics

Tiger Analytics

Tiger Analytics is a leading advanced analytics consulting firm partnering with Fortune 100 companies to drive business value through expertise in marketing science, customer analytics, and operations analytics.

Professional Services
1K-5K
Founded 2011
View All Jobs 37

Description

  • Design and define system architecture for ML- and AI-driven solutions across multiple business verticals.
  • Lead ML system design discussions and make high-level decisions for model serving, data pipelines, and MLOps frameworks.
  • Architect scalable and secure cloud-native platforms for model training, validation, deployment, and monitoring.
  • Build reusable components and reference architectures for multiple stages of the ML lifecycle.
  • Define and enforce best practices for model versioning, CI/CD, testing, and rollback strategies.
  • Deploy and manage machine learning and data pipelines in production environments.
  • Work on containerization and orchestration solutions for model deployment.
  • Collaborate with data scientists, engineers, product teams, and business stakeholders to develop and implement MLOps best practices.
  • Participate in fast iteration cycles and adapt solutions to evolving project requirements.
  • Communicate problem context and solution approach effectively with global and remote teams.

Requirements

  • Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
  • 10+ years of hands-on experience developing and applying advanced analytics solutions in a corporate environment.
  • At least 4 years of experience programming with Python.
  • At least 7 years of experience productionizing, monitoring, and maintaining models.
  • Strong programming skills in Python and ML libraries such as scikit-learn, TensorFlow, or PyTorch.
  • Deep experience with MLOps tools such as MLflow, Kubeflow, Airflow, SageMaker, or Vertex AI.
  • Hands-on experience designing ML systems on AWS, Azure, or GCP.
  • Strong understanding of data engineering, APIs, CI/CD pipelines, and model observability.
  • Excellent communication and stakeholder management skills.
  • Experience collaborating in cross-functional Agile teams and working with global remote teams.

Benefits

  • Opportunity for significant career development.
  • Fast-growing and challenging entrepreneurial environment.
  • High degree of individual responsibility.

Interested in this position?

Apply directly on the company website

Apply Now

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