We're looking for an
AI Engineer
to build and ship production-grade generative AI solutions: intelligent assistants, RAG systems, and autonomous agents. 100% hands-on role with end-to-end ownership: architecture, deployment, monitoring, and continuous improvement. Not research, not POCs — making it work in production.
What you'll do
Build LLM applications and AI agents with LangChain / LangGraph or similar.
Implement RAG pipelines: embeddings, vector databases, hybrid search.
Integrate multiple LLMs (OpenAI, Anthropic, Gemini, open source) based on cost, latency, and quality.
Deploy on cloud (AWS / GCP / Azure) and monitor performance, drift, and cost.
Implement LLM evals to ensure response quality.
✅ What we're looking for
4+ years developing with Python.
Real production experience with LLMs: prompt engineering, RAG, tool calling.
Hands-on with vector databases (Pinecone, Chroma, pgvector).
Cloud + solid engineering practices: testing, version control, CI/CD.
Intermediate/advanced English.
⭐ Nice to have
Agent frameworks (CrewAI, AutoGen) and protocols like MCP.
Fine-tuning of open-source models, MLOps.
Experience with US / European clients.

📌 Ai Engineer (Genai / Llm) - Latam (Buenos Aires)
🏢 Baireshr
📍 Buenos Aires