The AI Engineer will develop multi-agent workflows and production RAG systems, managing deployment and monitoring of AI solutions leveraging various tools and frameworks.
AI ENGINEER - Role Overview
We are looking for a hands-on AI/ML Engineer to implement production-grade, multi-agent systems. You will translate high-level architectural designs into robust Python code, focusing on reliability, latency, and the deterministic execution of agentic workflows.
This role can be remote in Greece or Poland, or hybrid in our Dublin office.
Key Responsibilities
- Develop Multi-Agent Workflows: Create agentic systems using frameworks like LangGraph and LangChain, implementing tool-calling, planning, and self-correction capabilities
- Build Production RAG Systems: Design and implement sophisticated retrieval-augmented generation pipelines with multi-modal capabilities, handling text, tables, and complex document structures. Integration of Graph Databases with LLM systems for entity extraction, relationship mapping, and structured retrieval.
- Production Deployment: Deploy and monitor AI systems using Docker, Kubernetes, and CI/CD pipelines, ensuring reliability, scalability, and cost-effectiveness
Technical Qualifications
Core Tech Stack
- Languages: Strong Python proficiency (asyncio, type hinting, Pydantic).
- AI Frameworks: Deep practical experience with LangChain, LangGraph, or LlamaIndex.
- Data Engineering: Experience with SQL and Vector Databases (Cosmos DB, Milvus, Chroma, or Pinecone, etc).
- API Development: Experience building FastAPI or Flask endpoints to expose agent workflows to the frontend.
Engineering Practices
- Containerization: Package AI applications and models in Docker containers with proper dependency management and optimization for production workloads
- CI/CD Pipeline Management: Build and maintain automated pipelines for testing, building, and deploying GenAI applications using Azure DevOps, GitHub Actions, or similar
- Kubernetes Orchestration: Deploy and manage containerized AI services on Kubernetes, handling autoscaling, resource allocation, and service mesh configuration
- Infrastructure as Code: Define and version control infrastructure configurations, deployment manifests, and cloud resources
- Version Control: Maintain clean Git workflows with proper branching strategies, meaningful commits, and thorough code reviews
- Monitoring & Observability: Implement logging, tracing, and metrics collection for production AI systems; set up alerts and dashboards
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