Full Stack AI Engineer

Job Summary The Full Stack AI Engineer will design and deliver AI-powered features across the entire product stack, integrating models, building agentic workflows, and implementing frontend and backend systems. This role sits at the intersection of AI, engineering, and product, and requires ownership from data modeling to UI execution. The engineer will collaborate closely with AI, product, and data teams to build user-facing intelligence capabilities. Key Responsibilities Agentic

AI & LLM

Systems Design and maintain AI agents that automate multi-step workflows. Build retrieval pipelines, semantic search systems, and RAG architectures grounded in structured business data. Implement guardrails, evaluation systems, and human-in-the-loop controls. Integrate LLMs into user-facing features across the product surface. Full Stack Engineering Build scalable applications across frontend and backend. Develop AI-native UX patterns such as copilots, chat interfaces, and workflow automation surfaces. Own features end-to-end, including data modeling, API design, and UI components. Platform & Infrastructure Deploy services using containerization and cloud infrastructure. Ensure reliability, performance, and observability of AI-powered features. Collaborate with ML teams to integrate model outputs into product workflows. Partner with Data Engineering to consume pipelines, APIs, and ETL outputs. Product Thinking & Iteration Rapidly prototype and iterate on AI-powered features. Participate in architecture decisions with a focus on impact and learning. Communicate tradeoffs clearly across technical and non-technical teams. Required Qualifications 5+ years of software engineering experience building production applications. Hands-on experience developing agentic AI workflows. Strong proficiency in React or Next.js and backend experience with Python or Node.js. Experience integrating LLMs or AI APIs into user-facing applications. Familiarity with RAG systems, vector databases, and embedding-based retrieval. Experience designing and documenting RESTful APIs. Proficiency in relational databases such as PostgreSQL. Knowledge of Kubernetes, Docker, CI/CD workflows, and observability practices. Experience with AI evaluation methods and output quality assessments. Ability to independently own features from concept to production. Preferred Qualifications Experience in startups or early engineering teams. Portfolio of AI projects such as copilots, RAG systems, or agents. Familiarity with music, rights management, or royalties workflows. Experience with AI-native development tools. Understanding of data privacy, safety, and responsible AI.