AI Engineer: LLMs, RAG & Local Inference

Aieng

Lazio

Ibrido

EUR 50.000 - 75.000

Tempo pieno

5 giorni fa
Candidati tra i primi
Generatore di candidature

Ottieni una risposta da questo datore di lavoro — un curriculum e una lettera di presentazione personalizzati, che corrispondono esattamente a ciò che sta cercando.

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Descrizione del lavoro

Aieng in Rome is seeking an AI Engineer to develop and implement AI-driven solutions focused on local inference systems and agentic workflows. You will integrate AI features into our .NET backend and participate in cross-functional software development.

The role emphasizes LLMs, NLP, RAG pipelines, and vector databases, with hybrid work participation and collaboration across engineering teams.

Competenze

  • 2+ years of proven software engineering experience in Applied AI or inference pipelines.
  • Degree in Computer Science, AI, or related field; advanced degrees are a plus.
  • Experience with Python and C#/.NET for cross-ecosystem integration.
  • Familiarity with LLMs, NLP, and AI deployment on local inference systems.
  • Experience with vector databases and RAG pipelines.

Mansioni

  • AI Deployment & Inference: deploy and run open-weight models in our platform.
  • RAG & Agentic Workflows: develop reasoning agents and retrieval pipelines.
  • Model Fine-Tuning: adapt and fine-tune foundational models for our domains.
  • Cross-Functional Collaboration: build AI services in Python that integrate with C# backend.

Conoscenze

Python
C#/.NET
LLMs
NLP
Vector databases
Multi-agent orchestration

Formazione

Bachelor's degree in CS/AI

Strumenti

Qdrant
Milvus
Weaviate
Ollama
vLLM

Descrizione del lavoro

Aieng in Rome is seeking an AI Engineer to develop and implement AI-driven solutions focused on local inference systems and agentic workflows. You will integrate AI features into our .NET backend and participate in cross-functional software development.

The role emphasizes LLMs, NLP, RAG pipelines, and vector databases, with hybrid work participation and collaboration across engineering teams.

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