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# Senior Artificial Intelligence Specialist

- **Company:** GR8_TECH
- **Location:** Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Cloud Computing, Continuous Integration, Data Infrastructure, Software Debugging, DevOps, Fault Tolerance, Python (Programming Language), Open Source Technology, Azure Machine Learning, Software Engineering, SQL Databases, Chatbots, Large Language Models, Multi-Agent Systems, Generative AI, Backend, Git, Machine Learning Operations, Docker
- **Published:** July 2, 2026
- **Apply:** https://es.indeed.com/viewjob?jk=feae48a540978348

## About the Role

* Strong software engineering background.
 * Proven experience building LLM-based systems in production.
 * Hands-on experience with: LLMs and RAG architectures, Chatbots or conversational AI systems, Tool/function calling and agent-style patterns.
 * Experience with context or tool-serving systems (MCP or similar).
 * Experience integrating AI into real-time production products.
 * Understanding of performance, scalability, and cost trade-offs.
 * Cloud experience (preferably AWS).
 * Strong engineering fundamentals (testing, debugging, reviews, observability).

Nice-to-have

 * Multi-agent or workflow-based systems.
 * Fine-tuning or adapting open-source LLMs.
 * ML platforms / MLOps background.
 * Experience operating latency- or cost-sensitive systems.
 * Production incident ownership in AI systems.

Tech Stack:

 * Languages: Python, SQL.
 * LLMs: Amazon Bedrock, OpenAI, Anthropic, open-source models.
 * Frameworks: LangChain, LangGraph, Pydantic, Langfuse.
 * RAG: Embeddings, Qdrant / FAISS / OpenSearch / pgvector.
 * Cloud: AWS (ECS / EKS / Lambda, S3, OpenSearch).
 * DevOps: Docker, Git, CI/CD.

## Description

This role focuses on applied Generative AI: internal AI automation, chatbots, AI assistants, and MCP-based services already used in production. You will work on top of an existing ML and data platform, building systems that are reliable, maintainable, and practical to operate., * Own LLM-based systems from design to production support.

 * Design architectures for Generative AI systems (RAG, agents, tool/context serving).
 * Make clear trade-offs between quality, latency, cost, and complexity.
 * Set engineering standards for building and operating AI systems in production.
 * Act as a technical reference point for applied GenAI.

Hands-on AI Engineering

 * Build and maintain production LLM-powered systems.
 * Develop chatbots and AI assistants with predictable behavior.
 * Design and implement RAG pipelines (ingestion, embeddings, retrieval, generation).
 * Implement agent-style workflows with tool/function calling.
 * Build and integrate MCP servers or similar context/tool-serving components.
 * Integrate AI systems with backend services and ML infrastructure.

Production & Reliability

 * Design evaluation frameworks for LLM outputs.
 * Monitor LLM behavior, system health, and costs in production.
 * Address performance, scalability, and operational issues.
 * Handle production incidents and contribute to long-term fixes.
 * Design systems with failure modes, fallbacks, and graceful degradation.


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