Machine Learning Engineer job opportunity at Tiger Analytics Inc..

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Date2025-12-09T17:51:33.870Z bot
Tiger Analytics Inc. Machine Learning Engineer
Experience: 5-years
Pattern: Full-time
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United States Of America

Tiger Analytics is a global leader in AI and advanced analytics consulting, empowering Fortune 1000 companies to solve their toughest business challenges. We are on a mission to push the boundaries of what AI can do, providing data-driven certainty for a better tomorrow. Our diverse team of over 6,000 technologists and consultants operates across five continents, building cutting-edge ML and data solutions at scale. Join us to do great work and shape the future of enterprise AI. 5+ years of professional software development experience, with strong proficiency in Python, and applying software engineering and design principles (OOP, functional programming, design patterns, testing frameworks, CI/CD fundamentals). Deep understanding of cloud-based data platforms (Azure, Databricks etc.), including cluster configuration, Spark optimization techniques and best practices. Strong understanding of distributed data processing systems (Spark, Delta tables, cloud storage layers) with hands-on experience in building data pipelines, optimizing performance, and handling large-scale datasets. Exposure to DevOps and engineering hygiene practices such as containerization (Docker), infrastructure-as-code, CI/CD pipelines, and automated testing for workflows. Proven ability to work effectively in cross-functional teams (DS, DE, Cloud Ops, Product) with a proactive, inquisitive, and go-getter mindset Ability to translate ambiguous business or analytical requirements into scalable technical solutions, with solid grounding in code quality, reliability, observability, and engineering best practices. Additional qualifications (Nice to have): Experience in operationalizing and deploying machine learning models using production-grade MLOps frameworks (MLflow, AzureML, Databricks Model Serving), with a strong understanding of model lifecycle management such as versioning, lineage, monitoring, retraining workflows, and deployment automation. Familiarity with modern data and ML architecture patterns such as feature stores, vector stores, low-latency inference pipelines.

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