Junior Ai Engineer - Site: Rome
About Aieng Aieng is a high-growth innovative startup focused on delivering next-generation engineering solutions. We bridge the gap between ambitious vision and technical reality by investing heavily in research, development, and top-tier talent. We are building an intelligent automation platform based on modern microservices, where AI is an active participant in engineering and design processes.
Your Role
As a Junior AI Engineer, you will join our engineering team to help build AI-driven features for our platform, working on RAG pipelines, agentic and multi-agent workflows, and local LLM inference. With guidance from senior engineers, you will write Python services that integrate with our core C#/. NET backend.
What You'll Do
Build and improve RAG pipelines: document ingestion, embeddings, and retrieval from vector databases. Develop reasoning agents and multi-agent workflows, and evaluate their reliability. Help deploy and test open-weight models for local inference. Build Python AI services and expose them through clean REST APIs that integrate with our C#/. NET platform, in collaboration with the backend team.
Requirements
Good proficiency in Python and basic software engineering practices (Git, testing, REST APIs). Hands-on experience (even academic or personal) building RAG pipelines and agentic workflows with LLMs.
Practical knowledge of multi-agent architectures (e.g., orchestrating multiple specialized agents). Interest in integrating Python AI services with a C#/. NET backend; basic C# knowledge is a strong plus. Solid fundamentals in Machine Learning and NLP. Degree (or final year) in Computer Science, AI, Mathematics, Engineering, or a related field. 0–2 years of experience, including internships or thesis work. Please share a link to your projects!
Nice to Have
Experience with local inference tools (e.g., Ollama, v LLM, llama.cpp). Vector databases (e.g., Qdrant, Milvus, Weaviate). Fine-tuning (e.g., Lo RA), for example for domain-specific engineering drawing pipelines. Computer Vision or OCR experience.
Soft Skills
Engineering Mindset: Strong analytical, problem-solving, and communication skills, with a focus on delivering working, reliable features. Teamwork: Ability to work collaboratively in a hybrid environment, both in-person and remotely. Curiosity and willingness to learn fast in a rapidly evolving field.
What We Offer
Mentoring from senior engineers and real ownership on industrial AI projects. Hands-on growth in RAG, agentic systems, and local LLM inference. Workplace Hybrid Workplace. Main Site: Rome.
Your Role
As a Junior AI Engineer, you will join our engineering team to help build AI-driven features for our platform, working on RAG pipelines, agentic and multi-agent workflows, and local LLM inference. With guidance from senior engineers, you will write Python services that integrate with our core C#/. NET backend.
What You'll Do
Build and improve RAG pipelines: document ingestion, embeddings, and retrieval from vector databases. Develop reasoning agents and multi-agent workflows, and evaluate their reliability. Help deploy and test open-weight models for local inference. Build Python AI services and expose them through clean REST APIs that integrate with our C#/. NET platform, in collaboration with the backend team.
Requirements
Good proficiency in Python and basic software engineering practices (Git, testing, REST APIs). Hands-on experience (even academic or personal) building RAG pipelines and agentic workflows with LLMs.
Practical knowledge of multi-agent architectures (e.g., orchestrating multiple specialized agents). Interest in integrating Python AI services with a C#/. NET backend; basic C# knowledge is a strong plus. Solid fundamentals in Machine Learning and NLP. Degree (or final year) in Computer Science, AI, Mathematics, Engineering, or a related field. 0–2 years of experience, including internships or thesis work. Please share a link to your projects!
Nice to Have
Experience with local inference tools (e.g., Ollama, v LLM, llama.cpp). Vector databases (e.g., Qdrant, Milvus, Weaviate). Fine-tuning (e.g., Lo RA), for example for domain-specific engineering drawing pipelines. Computer Vision or OCR experience.
Soft Skills
Engineering Mindset: Strong analytical, problem-solving, and communication skills, with a focus on delivering working, reliable features. Teamwork: Ability to work collaboratively in a hybrid environment, both in-person and remotely. Curiosity and willingness to learn fast in a rapidly evolving field.
What We Offer
Mentoring from senior engineers and real ownership on industrial AI projects. Hands-on growth in RAG, agentic systems, and local LLM inference. Workplace Hybrid Workplace. Main Site: Rome.