Machine Learning Engineer
Tiger Analytics Inc.11 months ago
Remote, United StatesSenior
Responsibilities
- Build scalable machine learning and data solutions for enterprise clients.
- Develop and optimize distributed data pipelines using Spark, Delta tables, cloud storage layers, and large-scale datasets.
- Configure cloud data clusters and apply Spark performance optimization techniques.
- Translate ambiguous business and analytical requirements into scalable technical solutions.
- Apply software engineering practices involving code quality, reliability, observability, testing, and deployment automation.
- Operationalize and deploy machine learning models using production-grade MLOps frameworks, including model versioning, lineage, monitoring, retraining workflows, and deployment automation.
- Collaborate with data science, data engineering, cloud operations, and product teams.
Requirements
- At least 5 years of professional software development experience.
- Strong Python proficiency and knowledge of software engineering and design principles.
- Deep understanding of cloud-based data platforms such as Azure and Databricks, including cluster configuration and Spark optimization.
- Hands-on experience with distributed data processing systems, Spark, Delta tables, cloud storage layers, data pipelines, and large-scale datasets.
- Exposure to Docker, infrastructure-as-code, CI/CD pipelines, automated testing, and DevOps practices.
- Ability to develop reliable, observable, maintainable technical solutions and work effectively in cross-functional teams.
- Preferred experience with MLflow, AzureML, Databricks Model Serving, model lifecycle management, feature stores, vector stores, and low-latency inference pipelines.
Benefits
- Fully remote workplace in the United States.
- Significant career development opportunities in a small, fast-growing, entrepreneurial environment.
- High degree of individual responsibility.
Tech Stack
Categories
About Tiger Analytics Inc.
Tiger Analytics Inc. provides data and AI consulting services that deliver machine learning, analytics, and cloud data engineering solutions for large enterprises across industries, including life sciences. The company engages on strategy and implementation projects, from modern BI and data platforms to MLOps, helping Fortune 100 clients turn data into business outcomes. Founded in 2011 and headquartered in Santa Clara, it is privately held with operations across North America, Europe, and Asia-Pacific.