United States · hace 11 meses
Construirás soluciones escalables de datos y ML, incluyendo pipelines de datos en plataformas cloud y el despliegue de modelos de machine learning, trabajando con equipos multifuncionales.
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. Requirements • 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. Benefits Significant career development opportunities exist as the company grows. The position offers a unique opportunity to be part of a small, fast-growing, challenging and entrepreneurial environment, with a high degree of individual responsibility. Tiger Analytics provides equal employment opportunities to applicants and employees without regard to race, color, religion, age, sex, sexual orientation, gender identity/expression, pregnancy, national origin, ancestry, marital status, protected veteran status, disability status, or any other basis as protected by federal, state, or local law.
Publicada el 8 dic 2025
