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Machine Learning Engineer

1 month ago
United States
Full-time
Senior
Machine Learning Engineer
Software Development
Apache Spark Azure Databricks Docker Machine Learning MLflow MLOps Python
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 11

Description

  • Design, build, and maintain scalable cloud-based data platforms and data pipelines to process large-scale datasets.
  • Configure and optimize clusters and Spark jobs, applying Spark optimization techniques and best practices.
  • Implement software engineering best practices (OOP, functional programming, design patterns) and maintain high code quality through testing and CI/CD.
  • Collaborate closely with cross-functional teams including Data Science, Data Engineering, Cloud Ops, and Product to translate requirements into technical solutions.
  • Optimize performance of distributed data processing systems (Delta tables, cloud storage layers) and troubleshoot production issues.
  • Contribute to DevOps and engineering hygiene by containerizing components (Docker), supporting infrastructure-as-code, and building CI/CD pipelines and automated tests.
  • Ensure reliability, observability, and maintainability of data workflows through monitoring, logging, and automated validation.
  • Operationalize and deploy machine learning models using production-grade MLOps frameworks and support model lifecycle activities (versioning, monitoring, retraining) when applicable.

Requirements

  • 5+ years of professional software development experience.
  • Strong proficiency in Python and applied software engineering and design principles (OOP, functional programming, design patterns).
  • Experience with testing frameworks and CI/CD fundamentals to ensure code quality and deployment automation.
  • Deep understanding of cloud-based data platforms (e.g., Azure, Databricks), including cluster configuration and management.
  • Hands-on experience with distributed data processing systems (Spark), Delta tables, and cloud storage layers for large-scale datasets.
  • Proven experience building and optimizing data pipelines and improving performance at scale.
  • Exposure to DevOps practices such as containerization (Docker), infrastructure-as-code, CI/CD pipelines, and automated testing.
  • Demonstrated ability to work effectively in cross-functional teams with a proactive and inquisitive mindset.
  • Ability to translate ambiguous business or analytical requirements into scalable, reliable technical solutions.
  • Nice to have: experience operationalizing/deploying ML models with MLflow, AzureML, or Databricks Model Serving and familiarity with feature stores, vector stores, and low-latency inference patterns.

Benefits

  • Significant career development opportunities as the company grows.
  • Opportunity to work in a small, fast-growing, entrepreneurial environment with a high degree of individual responsibility.
  • Work on cutting-edge ML and data solutions at scale for Fortune 1000 clients.
  • Membership in a diverse, global team operating across five continents.
  • Equal employment opportunity and commitment to an inclusive workplace.

Interested in this position?

Apply directly on the company website

Apply Now

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