> Markdown version of [/jobs/ext/52623-senior-ai-engineer](https://www.wearedevelopers.com/jobs/ext/52623-senior-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Senior AI Engineer

- **Company:** Automat-it Ver todas las vacantes
- **Location:** Madrid, Spain
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon S3, Continuous Integration, DevOps, Amazon DynamoDB, Python (Programming Language), Machine Learning, Large Language Models, Multi-Agent Systems, Backend, Kubernetes, Functional Programming, Cloudwatch, Terraform, GPT, Docker
- **Published:** May 24, 2026
- **Apply:** https://www.opcionempleo.com/jobad/es02a7a17ef817442beda909ed63918a8d

## About the Role

* Solid experience with AWS in real projects (not just Console usage), including working with services like Bedrock, OpenSearch, Lambda, S3, or similar
 * Experience building RAG systems in practice, including retrieval logic, vector databases, and output quality improvements
 * Experience working with LLM frameworks or tools such as Amazon Bedrock, OpenAI, LangChain, or LlamaIndex
 * Strong Python skills and a good understanding of backend system design
 * Experience designing multi-agent systems or more complex orchestration workflows
 * Experience with vector databases (OpenSearch, pgVector, Pinecone, etc.)
 * Comfort working in fast-moving environments with short project cycles (weeks to a few months)
 * Strong communication skills and ability to work directly with clients and cross-functional teams
 * Ability to clearly explain technical decisions, limitations, and trade-offs in English (written and spoken)
 * Experience with AWS Bedrock advanced features (e.g. Knowledge Bases, agent orchestration) - an advantage
 * Experience with Kubernetes, Docker, IaC Terraform and CI/CD pipelines - an advantage
 * Background in Machine Learning or Data Science (including model training or fine-tuning) - an advantage

## Description

* Build and deliver production-ready GenAI systems on AWS, including RAG pipelines, retrieval logic, and LLM-based services
 * Design and implement agent-based or multi-step workflows for real use cases (not just prototypes)
 * Work closely with Solution Architects, DevOps, and client teams to turn ideas and POCs into scalable systems
 * Deploy, monitor, and improve ML/LLM systems in production, focusing on performance, cost, and reliability
 * Work with AWS services such as Bedrock, OpenSearch, Lambda, S3, DynamoDB, SageMaker, and CloudWatch
 * Adapt existing ML or GenAI code into production environments when needed
 * Continuously improve system quality, including retrieval performance, output consistency, and evaluation approaches
 * Operate in a fast-paced, project-based environment where you may own a project as the main engineer

What Success Looks Like in Year One:

 * Within 12 months, you are independently owning and delivering production GenAI systems on AWS, from initial design through deployment and iteration
 * Working directly with customers and making clear architectural decisions around performance, cost, and reliability
 * You are comfortable taking an ambiguous problem, turning it into a working system end-to-end, and explaining the trade-offs behind your approach


## Related Videos

- [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension)
- [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered)
- [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform)
- [Agentic AI - From Theory to Practice: Developing Multi-Agent AI Systems on Azure](https://www.wearedevelopers.com/videos/1532-agentic-ai-from-theory-to-practice-developing-multi-agent-ai-systems-on-azure)
- [HR ROBO SAPIENS: Decoding AI Agents and Workflow Automation for Modern Recruitment](https://www.wearedevelopers.com/videos/1470-hr-robo-sapiens-decoding-ai-agents-and-workflow-automation-for-modern-recruitment)
- [Streaming AI Responses in Real-Time with SSE in Next.js & NestJS](https://www.wearedevelopers.com/videos/1630-streaming-ai-responses-in-real-time-with-sse-in-next-js-nestjs)

## Related Articles

- [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer)
- [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)
- [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)
- [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)
- [What Industries Outside of AI Are Hiring The Most AI Experts?](https://www.wearedevelopers.com/magazine/98-what-industries-outside-of-ai-are-hiring-the-most-ai-experts)
- [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline)