Full-Stack Applied AI Engineers

TL;DR

Full-Stack Applied AI Engineers (AI/full-stack): Building AI-native products, internal platforms, prototypes, and production applications with an accent on agentic workflows, LLM applications, RAG systems, scalable web architecture, and systems design. Focus on translating ambiguous business and client needs into working software, integrating modern frontend and backend technologies, and making pragmatic tradeoffs between rapid delivery and long-term scalability.

Location: Remote roles listed for the United States and Warsaw, Poland

Company

hirify.global is a holding company that builds full-stack AI businesses, portfolio products, client solutions, and the Gravity software factory platform.

What you will do

• Build and ship full-stack, AI-native products, internal platforms, prototypes, and production applications.
• Design agentic AI applications, LLM workflows, RAG and GraphRAG systems, document-processing pipelines, and AI-powered automation.
• Develop modern frontend experiences and backend APIs using React, Next.js, Python, Node.js, FastAPI, Django, and related technologies.
• Design data models and work with relational, graph, vector, and hybrid database architectures.
• Translate ambiguous product, business, and client needs into scalable technical systems while balancing speed and maintainability.
• Collaborate with founders, product leaders, engineers, portfolio companies, and clients to define problems and deliver zero-to-one products.

Requirements

• Strong software engineering fundamentals and experience shipping real products used by real users.
• Fluency in Python and experience building AI-powered applications, workflows, or systems.
• Full-stack development experience with modern web technologies such as React, Next.js, Node.js, FastAPI, or Django.
• Practical experience with LLMs, AI APIs, agent frameworks, integrations, cloud services, databases, and automation platforms.
• Ability to evaluate AI system quality and performance, design evolvable systems, and handle production edge cases.
• Strong communication, stakeholder collaboration, ownership, and comfort working with ambiguity.

Nice to have

• Experience with AI agents, RAG, GraphRAG, vector databases, knowledge retrieval, or document-processing pipelines.
• Experience building internal platforms that became external products or working in a forward-deployed engineering environment.
• Founder, founding engineer, or early-stage startup experience.
• Experience in regulated or technically complex industries, or products for operational and knowledge-worker teams.
• Open-source AI or ML contributions and a STEM, data science, or quantitative engineering background.

Culture & Benefits

• High ownership, autonomy, and responsibility for outcomes rather than assigned tickets.
• Bias toward shipping, rapid experimentation, and iteration based on real user feedback.
• Pragmatic engineering focused on leverage, simplicity, and compounding value.
• Asynchronous-first work with clear communication and tight feedback loops.
• Small teams working across AI, product, infrastructure, and company creation.
• Compensation is determined by location, experience, skills, internal equity, and market conditions; regional tiers apply.