
Artificial Intelligence • Finance • SaaS
Genios AI is an AI-powered SaaS platform that automates financial analysis and pitch-deck creation, turning weeks of spreadsheet work into minute-long outputs. Trained on successful deal books, it assembles comps, TAM visuals, and executive-ready narratives so finance teams can accelerate deal execution and reduce manual formatting. The company emphasizes automation, citizen enablement, and an agentic backend for intelligent financial workflows.
11 - 50 employees
🤖 Artificial Intelligence
💸 Finance
☁️ SaaS
November 25

Artificial Intelligence • Finance • SaaS
Genios AI is an AI-powered SaaS platform that automates financial analysis and pitch-deck creation, turning weeks of spreadsheet work into minute-long outputs. Trained on successful deal books, it assembles comps, TAM visuals, and executive-ready narratives so finance teams can accelerate deal execution and reduce manual formatting. The company emphasizes automation, citizen enablement, and an agentic backend for intelligent financial workflows.
11 - 50 employees
🤖 Artificial Intelligence
💸 Finance
☁️ SaaS
• You will be responsible for developing our core data platform. • This includes building scalable data ingestion connectors/pipelines, developing efficient APIs and ensuring the robustness and security of data and cloud based infrastructure. • You will build the large scale infrastructure that powers up crawling, ranking and retrieval systems.
• 4+ years of experience as a software engineer, with a focus on data platforms or infrastructure. • Expertise in Python and SQL • Mastery of distributed systems and proficiency in NoSQL and SQL tools. • Proven experience deploying and managing mission-critical ETL pipelines with diverse and large-scale data sources. • Experience with orchestration frameworks like Airflow • Proficiency in AWS or other cloud environments, with practical knowledge of their services. • Demonstrated ability to design and monitor secure and scalable systems, especially in production-grade environments. • Experience with containerization (Docker) and orchestration (Kubernetes) to streamline deployments. • Familiarity with LLMs and their potential applications
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