Artificial Intelligence Engineer

Roles & Responsibilities:

AI Platform & RAG

• Build and maintain enterprise AI applications and assistants for engineering, sales, marketing, procurement, and support.
• Build RAG systems on company knowledge bases.
• Build document ingestion pipelines for PDFs, manuals, brochures, and specs using tools like Docling and LlamaParse.
• Create and maintain vector databases (Qdrant), with embedding and reranking models (e.g. BGE-M3, BGE-Reranker).
• Tune retrieval pipelines for accuracy and evaluate answer quality (e.g. RAGAS).

Agentic AI

• Build autonomous, multi-step AI agents using LangGraph or similar.
• Integrate agents with internal systems (ERP, CRM) via MCP servers.
• LLM Integration
• Integrate self-hosted (DeepSeek, Qwen via vLLM) and API LLMs (OpenAI, Claude, Gemini, OpenRouter).
• Optimize prompts; apply safety controls (e.g. NeMo Guardrails).

Preferred candidate profile

We are building a self-hosted, enterprise-grade AI platform for our manufacturing, engineering, sales, and marketing teams. We need a hands-on AI Engineer to help build and maintain it working across LLMs, RAG, autonomous agents, vector databases, document intelligence, and GPU-based model serving, using AI coding assistants like Claude Code and Codex to move fast. This is a builder's role: you will ship production systems, not prototypes.

Workflow Automation

• Build business automation in n8n.
• Connect AI with ERP, CRM, email, WhatsApp, and internal apps.

Backend & Infrastructure

• Build REST APIs with FastAPI.
• Deploy with Docker and Docker Compose; work with GPU-based model serving (vLLM).
• Monitor with observability tooling (Langfuse, Grafana, Prometheus).