Fullstack ai engineer
Pubblicato il 15-08-2026 - Azienda Anonima in Italia
A fast-growing Italian startup is looking for an experienced AI Engineer - Full Stack to join its team and contribute to the design and development of next-generation AI-powered products.
La descrizione completa del lavoro illustra tutte le competenze, l'esperienza pregressa e le qualifiche che i candidati dovrebbero possedere.
This role is ideal for someone who is comfortable building end-to-end Generative AI systems, from data ingestion and knowledge retrieval to production deployment, while collaborating effectively in a dynamic and fast-paced startup environment.
- Design, develop, and maintain LLM-based Generative AI pipelines, ensuring high response quality, reliability, and low latency.
- Build and optimize Retrieval-Augmented Generation (RAG) solutions, including chunking strategies, embeddings, vector search, semantic retrieval, and re-ranking.
- Design and implement Graph RAG architectures leveraging knowledge graphs and semantic relationships to improve reasoning and traceability.
- Develop AI Agents and orchestrated workflows using function calling, tool usage,
and multi-step reasoning.
- Integrate leading commercial and open-source LLMs (e.g. OpenAI, Anthropic, Google Gemini, Llama, Mistral), evaluating performance, quality, and cost trade-offs.
- Design and develop scalable REST APIs and backend services exposing AI capabilities to frontend applications and third-party integrations.
- Work with vector databases (Pinecone, Weaviate, Milvus, pgvector) and graph databases (Neo4j) to support knowledge management and intelligent retrieval.
- Collaborate with frontend engineers to deliver conversational interfaces and AI-powered user experiences.
- Optimize AI applications for scalability, reliability, and cost efficiency using cloud-native architectures on AWS or GCP.
- Implement security best practices, authentication mechanisms, and prompt injection mitigation techniques.
- Monitor and evaluate AI systems using model evaluation frameworks, RAG quality me