Generative AI Engineer - Full-Stack & RAG Systems

Azienda Anonima

Ferrara

In loco

EUR 60.000 - 90.000

Tempo pieno

3 giorni fa

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Descrizione del lavoro

Azienda Anonima is seeking an experienced AI Engineer – Full Stack to design and deploy end-to-end AI-powered products in a fast-growing Italian startup. You will build Generative AI pipelines and RAG solutions, integrating diverse LLMs and vector/graph databases with scalable backend services.

The role requires strong Python/Node.js skills, deep GenAI expertise, and collaboration with frontend teams to deliver AI-powered user experiences.

Competenze

5+ years of software engineering experience with Generative AI technologies.
Hands-on with Python (FastAPI) and/or Node.js (Express or NestJS).
Experience with LLMs, prompt engineering, and AI orchestration frameworks such as LangChain, LangGraph, or LlamaIndex.
Strong knowledge of RAG, embeddings, semantic search, vector databases, and knowledge management architectures.
Experience with PostgreSQL, MongoDB, and vector database technologies.
Knowledge of frontend technologies such as React or Vue.js, TypeScript, and modern web development practices.
Experience with Docker, Kubernetes, CI/CD pipelines, and cloud platforms such as AWS or Google Cloud Platform.
Familiarity with REST APIs, GraphQL, authentication mechanisms, and scalable distributed architectures.
Experience monitoring AI applications, evaluating model performance, and optimizing latency, quality, and infrastructure costs.
Strong software engineering fundamentals, including clean, maintainable code, testing, documentation, and code reviews.

Mansioni

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 metrics, and LLM evaluation methodologies.
Troubleshoot and improve the entire AI lifecycle, from data ingestion through production deployment.