9 часов назад
Backend Engineer (AI)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Backend Engineer (AI) (TypeScript/Python): Design and deliver secure, scalable backend services, APIs, and microservices for cloud-based intelligent decisioning products with an accent on LLM integration, agentic AI, and high availability. Focus on building modular cloud-native architectures, integrating AI capabilities into backend workflows, and optimizing performance, security, and deployment reliability.
Company
develops cloud-based intelligent decisioning solutions for insurers and banks, covering pricing, rating, underwriting, and product personalization.
What you will do
- Design, develop, deliver, and document secure and scalable backend services and APIs.
- Build and maintain highly available, high-performance microservices-based systems using cloud-native architectures.
- Integrate LLMs, AI capabilities, and agentic tools into backend workflows and product services.
- Collaborate with AI Product Owners, Architects, and LLMOps on technical solutions and architectural consistency.
- Apply TDD, clean code, continuous integration, and collaborative code review practices.
- Contribute to modular and extensible architectures that support future product growth.
Requirements
- 6+ years of backend software engineering experience.
- Strong proficiency in TypeScript and/or Python for backend development.
- Experience designing and deploying microservices in Microsoft Azure or AWS cloud environments.
- Hands-on experience with Docker, Kubernetes, and ArgoCD.
- Knowledge of LLM and AI tool integration into backend systems.
- Understanding of CI/CD pipelines, security best practices, performance optimization, Kanban, and collaborative development.
Nice to have
- Familiarity with AI agent frameworks, LangGraph, MCP, or similar technologies.
Culture & Benefits
- Cross-functional collaboration with product, architecture, and LLM operations specialists.
- Emphasis on proactive problem-solving, technical quality, and system stability.
- Support for continuous learning and skills development.
- Collaborative development practices including code reviews and Kanban methodology.
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