GenAI & LLMs AI Engineer

Siemens AG; Lisboa; Hybrid; EUR 30,000 - 48,000; Full time

Benefits offered by this job

Belong & Innovate

Flexibility that Works

Health & Well-being

Grow Without Limits

Purpose & Perks

Job summary

Siemens Mobility Lisbon is seeking a talented AI/ML engineer to design, develop, and improve AI solutions across domains. You will build AI applications and pipelines using GenAI, LLMs, RAG, and agent-based workflows, translating business needs into practical AI-driven results.

Collaborate with stakeholders and technical teams to deploy production-ready AI services on cloud platforms, monitor performance, and drive continuous improvement that delivers measurable business value.

Qualifications

• Bachelor's or Master's in CS/DS/ML or related field.
• 1–3 years in software engineering, AI/ML or data science.
• Strong Python development skills.
• Knowledge of GenAI concepts (LLMs, embeddings, RAG).
• Experience with cloud platforms (Azure OpenAI, AWS Bedrock) and LangChain.

Responsibilities

• Design, develop, and improve AI solutions across domains.
• Build AI applications and workflows with GenAI, LLMs, RAG, agent-based systems.
• Collaborate with stakeholders to translate requirements into AI-driven results.
• Develop, test, and maintain AI/ML pipelines and cloud-native services.
• Deploy, monitor, and improve AI apps to deliver measurable business value.
• Leverage cloud platforms to create scalable, secure AI solutions.
• Stay up to date with AI landscape and share best practices.
• Work with experienced AI engineers, software developers, and architects.

Skills

Python

GenAI concepts

LLMs

RAG

LangChain

LangGraph

Promptflow

APIs

Azure AI

AWS Bedrock

Cloud platforms

Education

Bachelor's/Master's in CS/DS/ML

Tools

Azure OpenAI

Azure AI services

AWS Bedrock

LangChain

LangGraph

Promptflow

Job description

Siemens Mobility Lisbon is seeking a talented AI/ML engineer to design, develop, and improve AI solutions across domains. You will build AI applications and pipelines using GenAI, LLMs, RAG, and agent-based workflows, translating business needs into practical AI-driven results.

Collaborate with stakeholders and technical teams to deploy production-ready AI services on cloud platforms, monitor performance, and drive continuous improvement that delivers measurable business value.