ppAutomat-it is an all‑in AWS Premier Partner and Managed Services Provider specializing in the startup ecosystem. With over 800 customers and 500+ AWS certifications, Automat-it brings hands‑on expertise in AI, DevOps, and FinOps to empower fast‑paced startups to grow, deliver win. Our customers save significant time‑to‑market and optimize their cloud performance and costs. /p pWe work across EMEA and the US, fueling innovation and solving complex challenges daily. Join us to grow your skills, shape bold ideas, and help build the future of tech. /p pWe’re looking for an AI Engineer (Senior level or strong Middle) to join our team and work directly with startup's Data Science and RD teams. This is a hands‑on, delivery‑focused role where you will own projects end‑to‑end, from early design to production deployment. /p pThe role is focused on building production‑grade Generative AI systems on AWS, especially RAG pipelines, agent‑based workflows, and LLM‑powered backend services. This is not a pure research or model training role, it’s about designing and shipping reliable systems under real constraints. /p pWork location: hybrid from Madrid. /p pCurious about what it's really like to work at Automat‑it? /p pExplore our benefits, culture, and what success in your first year could look like here. /p h3Key Responsibilities: /h3 ul liBuild and deliver production-ready GenAI systems on AWS, including Amazon Bedrock, AgentCore, RAG systems, intelligent document processing, voice AI, and LLM-powered services. /li liDesign and implement AI agents using Amazon Bedrock AgentCore, AWS Strands, MCP, and modern orchestration frameworks for real customer solutions. /li liWork closely with Solution Architects, DevOps,



and customer teams to turn discovery workshops, ideas, and POCs into production-ready AI systems. /li liEvaluate and select the most appropriate LLMs based on accuracy, latency, cost, and customer requirements. /li liBuild reusable AI components and deployment patterns that accelerate future customer projects. /li liDeploy, monitor, and improve ML/LLM systems in production, focusing on performance, cost, and reliability /li liWork with AWS services such as Bedrock, OpenSearch, Lambda, S3, DynamoDB, SageMaker, and CloudWatch /li liAdapt existing ML or GenAI code into production environments when needed /li liContinuously improve system quality, including retrieval performance, output consistency, and evaluation approaches /li liOperate in a fast‑paced, project‑based environment where you may own a project as the main engineer /li /ul h3Requirements: /h3 ul liStrong hands‑on experience building and deploying AI / GenAI systems in production /li liStrong hands‑on AWS experience beyond model invocation, including infrastructure, IAM, serverless services, networking, storage, monitoring, and production deployments using Amazon Bedrock. /li liExperience building RAG systems in practice, including retrieval logic, vector databases, and output quality improvements /li liStrong understanding of modern LLM ecosystems,



including commercial and open‑source models, their trade‑offs, deployment options, and production use cases. /li liStrong Python skills and a good understanding of backend system design /li liExperience designing multi‑agent systems or more complex orchestration workflows /li liExperience with vector databases (OpenSearch, pgVector, Pinecone, etc.) /li liComfort working in fast‑moving environments with short project cycles (weeks to a few months) /li liStrong communication skills and ability to work directly with clients and cross‑functional teams /li liAbility to clearly explain technical decisions, limitations, and trade‑offs in English (written and spoken) /li liHands‑on experience with Amazon Bedrock Knowledge Bases, AgentCore, Agents, AWS Strands, or MCP is a strong advantage. /li liExperience selecting, evaluating and optimizing LLMs for quality, latency and cost. /li liAbility to explain technical trade‑offs and guide customers through AI solution design is a strong advantage. /li liExperience with Infrastructure as Code (Terraform, CloudFormation or AWS CDK), Docker, Kubernetes and CI/CD pipelines is a strong advantage. /li liExperience with speech‑to‑text, text‑to‑speech or Voice AI is an advantage. /li liBackground in Machine Learning or Data Science (including model training or fine‑tuning)- an advantage /li /ul pAutomat‑it is committed to fostering a workplace that promotes equal opportunities for all. We firmly believe that cultivating a diverse workforce is crucial to our success. Our recruitment decisions are grounded in your experience and skills, recognizing the value you bring to our team. /p /p #J-18808-Ljbffr

📌 AI Engineer (Madrid)
🏢 Automat-IT
📍 Madrid