Note: The job is a remote job and is open to candidates in USA. Genios AI is on a mission to build an AI-native platform for finance, transforming how investment teams operate. The Backend Engineer will architect and develop scalable backend systems, build APIs, and collaborate with cross-functional teams to integrate financial logic and user workflows.

Responsibilities

• Architect and develop scalable backend systems using Python and modern cloud-native tools
• Build APIs, orchestration layers, and data pipelines that support autonomous agents and real-time financial analytics
• Translate complex financial logic into clear, maintainable, and performant code
• Collaborate with cross-functional teams—including ML, data, and product—to integrate LLMs, financial logic, and user workflows
• Own the performance, reliability, and maintainability of mission-critical systems
• Drive best practices in architectural decisions, testing, devops practices, IaC and system observability

Skills

• Have 3–7 years of backend engineering experience
• Have built and scaled production-grade backend systems for data-intensive or real-time applications
• Are excited about building intelligent systems with autonomous behavior, not just CRUD apps
• Have strong proficiency in Python and experience building distributed backend systems
• Think in abstractions and API contracts before diving into implementation, and have experience designing and consuming clean, well-structured interfaces (REST, GraphQL, or RPC)
• Have experience with containerization (Docker), orchestration (Kubernetes), and event-driven systems (e.g., Kafka)
• Have deep understanding of relational databases (e.g., PostgreSQL) and caching systems (e.g., Redis)
• Have a strong grasp of performance tuning, code quality, automated testing, CI/CD practices, and building systems that are secure, observable, and production-ready
• Enjoy working on complex logic and domain modeling in high-stakes environments like fintech, trading, or enterprise SaaS
• Experience with AI frameworks, LLMs, or multi-agent orchestration platforms (e.g., LangGraph, CrewAI, AutoGen, Haystack, etc.)
• Exposure to financial data modeling, investment workflows, or alternative data pipelines

Company Overview