Вакансия из Hirify Global, списка международных tech-компаний Для мэтча и отклика нужен Plus
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Зарплата не указанаПередовые технологииПонятные задачиРост и обучение
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Описание вакансии
Текст:
Hirify AI/Оригинал
TL;DR
Software Engineer (Generative AI and Innovation) (AI/LLMs): Building, testing, and scaling production-ready applications that solve business and client problems with an accent on generative AI, software engineering, data integration, and automation. Focus on designing LLM workflows, evaluation harnesses, RAG pipelines, agent patterns, and reusable components while improving the quality, reliability, latency, and cost-efficiency of AI systems.
Location: Dublin, Ireland; hybrid work with a requirement to be in the office three days per week
Company
hirify.global is a global professional services organization providing risk, reinsurance, capital, people, investments, and management consulting services in 130 countries.
What you will do
Own medium-scale features and components from prototyping through production-ready implementation.
Build AI-powered and other practical applications that address business and client needs.
Design and implement LLM workflows, prompt-engineering practices, tool use, structured outputs, RAG pipelines, and agent patterns.
Build and maintain evaluation harnesses, reusable components, tools, and engineering patterns.
Collaborate with Dublin and international Innovation teams in an Agile environment.
Contribute to code reviews, pairing, hackathons, knowledge sharing, and the growth of junior colleagues.
Requirements
2–5 years of software engineering experience and a track record of independently delivering production-grade features.
Hands-on JavaScript or TypeScript development with React, Vue, or Angular, plus backend experience with Node.js, NestJS, or similar technologies.
Experience using Python to build AI-powered workflows, integrations, or applications.
Experience building with LLMs, including prompt design, tool use, structured outputs, RAG, retrieval pipelines, and embeddings.
Working knowledge of AWS and Kubernetes, with an understanding of deploying and scaling AI applications.
Strong communication, practical problem-solving, and the ability to work across engineering and business teams.
Nice to have
Experience taking features from early experimentation through production use.
Curiosity about agent ecosystem standards, including Model Context Protocol.
Exposure to vector databases, semantic search, AI evaluation frameworks, testing approaches, and guardrails.
Attention to AI system quality, reliability, latency, and cost-efficiency.
Culture & Benefits
Hybrid work with office collaboration, connections, and professional development benefits.
Professional development opportunities, interesting work, and supportive leadership.
Inclusive culture focused on experimentation, learning, collaboration, and impact.
Access to the resources and career opportunities of a global organization.
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