AI Engineer (Agentic AI)

Quick Facts

• Focus: agentic AI solutions using LLM, RAG, and multi-agent architectures
• Backend development: Python services and integrations via APIs
• Emphasis: evaluation, monitoring/observability, and safety controls

Description

You will design, develop, and maintain production-ready agentic AI solutions used in the customer’s business processes. The role includes building LLM-based systems, implementing RAG, integrating multi-agent architectures, and improving answer quality with strong evaluation, observability, and safety practices.

Requirements

• Minimum 5 years of commercial Python programming experience
• Practical experience designing and deploying LLM solutions
• Practical experience building RAG solutions
• Experience designing and implementing Agentic AI solutions
• Working knowledge of Agno, LangGraph, LangChain, LlamaIndex (or equivalents)
• Experience integrating AI solutions with business systems via APIs
• Knowledge of ML/AI libraries/frameworks: PyTorch, TensorFlow, Hugging Face
• Practical knowledge of vector databases: Qdrant, ChromaDB, Weaviate, Milvus (or equivalents)
• Knowledge of embeddings, reranking, and optimizing model answer quality
• Hands-on experience building backend services with FastAPI
• Knowledge of AI observability and evaluation (e.g., Langfuse, LangSmith, Promptfoo, DeepEval)
• Knowledge of Docker and Kubernetes/OpenShift
• Knowledge of MLOps and LLMOps
• Ability to design AI solution architecture in an enterprise environment
• Ability to collaborate with business and technical teams
• Required experience summary: at least 2 years in designing and deploying LLM/RAG/Agentic AI solutions; experience delivering production AI projects; experience integrating into enterprise architecture; preferred finance/banking or other regulated environments

Benefits

• Long-term collaboration
• Benefits package: Multisport, private medical care, life insurance
• Training budget
• Free English lessons
• Individual support from a dedicated company mentor