AI Engineer

November 12, 2024

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TigerEye

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TigerEye is a modern business intelligence platform that utilizes AI to provide instant and accurate insights into business growth and performance. It is designed to cater to go-to-market (GTM) teams, offering capabilities such as pipeline analysis, sales forecasting, and marketing funnel collaboration. With features like TigerChat, an AI-driven conversational analyst, TigerEye helps users make faster, smarter decisions by delivering answers to complex questions related to pipeline, accounts, revenue, and more. The platform enables strategic planning, collaborative execution, and roles-based data access, making it an essential tool for businesses aiming to scale and optimize their operations.

b2b • startups • sales • technology • revenue platform

11 - 50 employees

🤖 Artificial Intelligence

🏢 Enterprise

☁️ SaaS

📋 Description

• TigerEye is an AI Analyst for go-to-market. • Track changes in business to deliver answers through an app. • Design, develop, and validate statistical models. • Own training, integration, deployment, versioning, and monitoring of ML components. • Improve metrics collection and analysis techniques. • Build a customizable heuristic system to surface insights. • Provide quantitative rationale for decision-making processes. • Design and build ML infrastructure, including customer data integration.

🎯 Requirements

• Substantial experience in programming languages such as Go, Dart, and Python and ability to write production level code • Substantial experience building with SQL • A degree in Computer Science, Statistics, Applied Mathematics, or a related field • Substantial experience with statistics and data modeling • Knowledge of statistical techniques to build predictive models • Knowledge of time series analysis • Solid understanding of computer science, coding principles and algorithms • Experience with the full lifecycle of building a ML-powered product • Experience building and evaluating multi-agentic AI apps with OpenAI/LangChain APIs • Experience building ML infrastructure • Experience with data engineering and building an ETL pipeline • Experience with cloud computing services (AWS, GCP, etc.)