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ML Ops Architect

3 weeks, 3 days ago
United States
Full-time
Senior
Machine Learning Engineer
DevOps and Infrastructure
Ansible Apache Airflow Apache Spark AWS Azure CI/CD GCP GitHub Machine Learning MLflow MLOps Power BI Python REST API Tableau Terraform
Apply Now
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 86

Description

  • Implement scalable cloud-based systems to support model inference at scale.
  • Deploy, manage, and maintain machine learning and data pipelines in production environments.
  • Build containerization and orchestration solutions for model deployment.
  • Collaborate with cross-functional Agile teams to develop software for big data and machine learning applications.
  • Implement CI/CD practices, including test automation and monitoring, for ML models and application code.
  • Establish best practices for MLOps, including version control, model versioning, monitoring, alerting, and automated deployment.
  • Manage and monitor machine learning infrastructure to ensure high availability and performance.
  • Build robust monitoring and logging solutions to track model performance and system health.
  • Troubleshoot and resolve production issues related to ML deployment, performance, scalability, and reliability.
  • Develop documentation, standard operating procedures, and guidelines for MLOps processes and tools.

Requirements

  • Master's or doctoral degree in computer science, electrical engineering, mathematics, or a similar field.
  • 7+ years of hands-on experience developing and applying advanced analytics solutions in a corporate environment.
  • 4+ years of experience programming with Python.
  • 3+ years of experience designing and building data-intensive solutions using distributed computing.
  • 3+ years of experience productionizing, monitoring, and maintaining models.
  • Experience with Azure services such as Azure Machine Learning, Azure Data Factory, Azure Databricks, Azure Kubernetes Service, and Azure Monitor.
  • Experience building and deploying AI and machine learning solutions at scale on AWS, Azure, or Google Cloud Platform.
  • Experience developing and maintaining APIs, such as REST APIs.
  • Experience specifying infrastructure and infrastructure as code tools such as Ansible or Terraform.
  • Experience with tools and technologies such as Python, Spark, Databricks, GitHub, MLflow, and Airflow.
  • Experience with Unix shell scripting and dependency-driven job schedulers.
  • Understanding of security, compliance, and data privacy requirements in ML infrastructure.
  • Experience with visualization tools such as RShiny, Streamlit, Python Dash, Tableau, or Power BI.
  • Familiarity with data privacy standards, methodologies, and best practices.

Benefits

  • Significant career development opportunities as the company grows.
  • Opportunity to work in a small, fast-growing, challenging, and entrepreneurial environment.
  • High degree of individual responsibility.

Interested in this position?

Apply directly on the company website

Apply Now

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