Mid-Senior AI Engineer (GenAI)

TL;DR

Mid-Senior AI Engineer (GenAI): Designing and operationalizing enterprise-grade AI solutions, including conversational assistants, RAG systems, and intelligent document processing with an accent on scalable production-ready systems and AI-driven workflow automation. Focus on building semantic retrieval pipelines, implementing guardrails for safe AI behavior, and integrating LLMs into enterprise cloud environments.

Location: Hybrid in Ljubljana, Slovenia

Company

Global insurance provider focusing on using AI responsibly to enhance customer experience and decision-making.

What you will do

• Design and operationalize GenAI applications such as conversational assistants and copilots for business users.
• Develop intelligent document processing solutions using NLP and GenAI to extract and structure information from unstructured content.
• Implement retrieval-augmented generation (RAG) systems and enterprise search solutions across various data sources.
• Build AI-driven automation workflows and document ingestion pipelines, including chunking and embedding generation.
• Implement guardrails, evaluation frameworks, and monitoring systems to ensure safe and reliable AI behavior.
• Collaborate with data engineers and architects to deploy and scale AI solutions within Microsoft Azure.

Requirements

• At least 3 years of experience in software, data, or machine learning engineering.
• Strong programming skills in Python and proficiency in SQL.
• Hands-on experience delivering AI-powered solutions in production environments.
• Solid understanding of LLM application development, prompt engineering, and vector databases.
• Experience with Microsoft Azure services (Azure OpenAI, AI Search, AKS).
• Fluent written and spoken English.

Nice to have

• Background in insurance, financial services, or other regulated enterprise environments.
• Experience with orchestration frameworks like LangChain, Semantic Kernel, or Azure AI Agent Framework.
• Exposure to low-code AI platforms such as Microsoft Copilot Studio or Power Automate.
• Knowledge of MLOps/LLMOps practices and knowledge graph-based retrieval.

Culture & Benefits

• Flexible working arrangements tailored to individual needs.
• Commitment to responsible, transparent, and fair AI implementation.
• Inclusive environment that values uniqueness and equal opportunity.
• Innovative corporate culture focused on challenging the status quo.