Junior/Mid-Level Generative AI Engineer

🎯 We build practical GenAI solutions, including RAG applications, document assistants, tool-enabled workflows, and structured outputs. If you enjoy learning new technologies, writing clean code, and turning ideas into working solutions, this is a great opportunity to grow with our team.

You will work closely with experienced AI Engineers, Solution Architects, and LLMOps specialists. Your focus will be to contribute to implementation, testing, and continuous improvement while developing your GenAI engineering skills.

🔎 What you bring:

• with Python and software or GenAI application development, gained through employment, an internship, or substantial project work.
• Basic understanding of Generative AI, large language models, prompt design, and API integration.
• Some exposure to LangChain, LangGraph, LlamaIndex, or a similar framework is an advantage.
• Familiarity with RAG, embeddings, vector search, or document-processing concepts.
• Basic knowledge of REST APIs and backend development; experience with FastAPI or a similar framework is helpful.
• Understanding of software engineering fundamentals such as Git, testing, debugging, and clean code.
• Foundational understanding of cloud concepts, including compute, storage, networking, identity and access management, security, and deployment models; exposure to Azure, AWS, or GCP is expected.
• Curiosity, problem-solving skills, and willingness to learn from feedback.
• Ability to communicate clearly and collaborate in an international team.
• personal, academic, or internship projects involving chatbots, RAG, AI agents, NLP, or data applications.
• EU work permit is required.

🚀 What you will work on:

• Support the development of GenAI applications using Python and frameworks such as LangChain or LangGraph.
• Help build and test RAG pipelines for document search, summarization, and question answering.
• Connect AI applications to APIs, databases, and internal services with guidance from experienced team members.
• Write, test, debug, and document maintainable code.
• Assist with prompt design, structured outputs, guardrails, and basic evaluation of AI responses.
• Investigate issues using logs and tracing tools and contribute to improving solution quality.
• Learn cloud-based GenAI services on Azure, AWS, or GCP.
• Participate in code reviews, team discussions, knowledge-sharing sessions, and client projects.

Example use cases:

• help users query and understand enterprise data safely.
• build grounded document Q&A, summarization, and information-extraction features.
• support basic automated evaluations, regression tests, and tracing with tools such as LangSmith, Langfuse, or Datadog.
• contribute to solutions using services such as Azure OpenAI and Azure AI Foundry, or equivalent services on AWS and GCP.