Senior ML Platform Architect

Tiger Analytics; Town of Texas, Fort Worth (WI, TX); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Career development

Entrepreneurial environment

Job summary

Tiger Analytics, a leading analytics consulting firm, seeks a Senior ML OPS Engineer to join our team in the United States. You will build and operate ML platforms, scale data pipelines, and collaborate across data science, software, and infrastructure teams to deliver production-grade ML capabilities.

We value hands-on engineers who can deploy robust ML systems, implement monitoring and security best practices, and drive continuous improvement in a fast-growing environment.

Qualifications

• Master's or PhD in CS/EE/Math or related field.
• 7+ years of hands-on analytics solutions development in a corporate environment.
• 4+ years Python programming.
• 3+ years distributed data-intensive solution design.

Responsibilities

• Implement scalable cloud-based ML inference systems.
• Deploy and manage ML data pipelines in production.
• Work on containerization and orchestration for model deployment.
• Collaborate in an Agile cross-functional team.
• Adopt CI/CD, tests, and monitoring for ML deployments.
• Ensure governance, security and responsible AI practices.
• Develop MLOps processes: versioning, monitoring, alerting, deployment.
• Monitor ML infra for availability and performance.
• Troubleshoot production ML deployment issues.

Skills

Azure ML

Cloud platforms

Python

Distributed computing

REST APIs

CI/CD

ML tooling

Security & compliance

Education

Master's or PhD in CS/EE/Math

Tools

Terraform

Ansible

GitHub

MLFlow

Airflow

Job description

Tiger Analytics, a leading analytics consulting firm, seeks a Senior ML OPS Engineer to join our team in the United States. You will build and operate ML platforms, scale data pipelines, and collaborate across data science, software, and infrastructure teams to deliver production-grade ML capabilities.

We value hands-on engineers who can deploy robust ML systems, implement monitoring and security best practices, and drive continuous improvement in a fast-growing environment.