AI Engineer (Agentic AI / LLM / RAG)

Quick Facts

• Role: AI Engineer (Agentic AI / LLM / RAG)

Description

Design, develop, and maintain production Agentic AI solutions for business processes. Build multi-agent architectures, implement RAG, integrate AI agents into business systems, and improve evaluation, monitoring, safety, and the overall quality, cost, and efficiency of LLM outputs.

Responsibilities

• Design, develop, and maintain Agentic AI solutions used in business workflows.

• Build, develop, and maintain systems based on LLM models.

• Design and implement RAG solutions.

• Build multi-agent architectures using frameworks such as Agno, LangGraph, or equivalent approaches.

• Develop the Agentic AI platform based on the Agno framework.

• Integrate AI agents with business systems via API and integration services.

• Design and develop AI components in Python.

• Develop and maintain ML models that support Agentic AI solutions.

• Create mechanisms for evaluation, monitoring, and observability of AI solutions.

• Optimize answer quality, reduce hallucinations, and develop AI safety and control mechanisms.

• Optimize AI solution performance, costs, and quality.

• Collaborate with business, architecture, IT, and security teams.

Requirements

• Minimum 5 years of commercial experience programming in Python.

• Minimum 2 years of commercial experience designing and deploying production solutions based on LLM, RAG and/or Agentic AI.

• Practical experience designing and implementing AI agents and multi-agent architectures.

• Knowledge of AI frameworks such as Agno, LangGraph, LangChain, LlamaIndex or equivalents.

• Experience integrating AI solutions with business systems through APIs and building backend services with FastAPI.

• Knowledge of ML/AI libraries and frameworks, especially PyTorch, TensorFlow, and Hugging Face.

• Hands-on knowledge of vector databases (Qdrant, ChromaDB, Weaviate, Milvus or equivalents).

• Knowledge of embeddings, reranking, and improving model answer quality.

• Experience with evaluation, monitoring, and observability for AI systems (e.g., Langfuse, LangSmith, Promptfoo, DeepEval).

• Knowledge of Docker, Kubernetes/OpenShift, and MLOps/LLMOps concepts.

• Ability to design AI architectures in an enterprise environment and collaborate with business and technical teams.

Benefits

• Stable employment with long-term projects and growth opportunities across IT areas.

• Flexible engagement forms: B2B contract, employment contract, or contract work.

• Benefits: Medicover (including dentistry), FitProfit, English lessons.

• F45 gym training at the office with professional coaches.

• Internal employee referral program.

• Funding support for IT certifications.

• Hybrid/remote/on-site work depending on the project.

• Team events and integration trips.

• Support for charitable initiatives (e.g., Szlachetna Paczka) and #PomagamyNexio.