Applied AI and Infrastructure Engineer

• Demonstrable experience of 3 to 5 years building AI systems in production, with real experience taking solutions from prototype to production environments.

• Solid knowledge of LLMs, including prompting techniques, skill generation, and building and integrating MCPs.

• Experience designing RAG systems, including chunking, embeddings, vector databases, and integration with language models.

• Experience building agentic systems using frameworks such as LangGraph, CrewAI, AutoGen, or similar, as well as their orchestration.

• Strong Python skills, with the ability to write clean, maintainable, and tested code.

• Solid knowledge of infrastructure and DevOps: Docker, CI/CD, Git, and the Linux command line.

• Experience designing and consuming REST APIs using FastAPI, Flask, or equivalent technologies.

• DevOps mindset and operations orientation: the ability to work not only at the application layer but also on the deployment, maintenance, and evolution of systems.

• Professional-level Spanish and sufficient English to work with technical documentation and libraries.

• Experience with infrastructure as code, especially Terraform, Ansible, or equivalent tools.

• Knowledge of cloud (AWS, Azure, or GCP), especially compute, storage, and networking services.

• Experience in observability, using tools such as Prometheus, Grafana, and different logging and tracing stacks.

• Knowledge of GitOps, Helm, and deployment automation.

• Experience in LLM evaluation and observability, including evals, quality metrics, and hallucination detection.

• Experience with vector databases such as pgvector, Pinecone, Weaviate, Qdrant, or similar.

• Familiarity with MLOps and data pipelines oriented toward Artificial Intelligence projects.

• Experience in model fine-tuning, including techniques such as SFT, LoRA, or equivalent.

• Personal projects, open source contributions, or a technical portfolio on GitHub.

✅ Permanent contract in a leading educational group in continuous growth.
📍 Hybrid work model and flexible working hours.
💻 Joining a team that works with AI applied to real production, within an expanding company.
🚀 Work with current technology: applied Artificial Intelligence, LLMs, agents, containers, Kubernetes, and cloud.
🎓 Development plan and continuous training, with access to our educational platforms.
💰 Competitive salary commensurate with the experience and knowledge you bring.
🤝 A technology environment where you can take part in real projects and take responsibility for the solutions you build.

📩 We want to meet you!

If you are passionate about applied Artificial Intelligence, enjoy building solutions that reach production, and want to work combining AI, engineering, and infrastructure, this is your opportunity!

📢 We are looking for IT talent specialized in Artificial Intelligence and infrastructure!

At Grupo PROEDUCA, a leader in online higher education, we are looking to bring on board an Applied AI and Infrastructure Engineer to join our technology team and take part in building, deploying, and operating Artificial Intelligence systems in production.

We are looking for a builder profile, someone capable of taking an idea from proof of concept to a real, stable, and scalable system. A professional with solid knowledge of applied AI and a technical foundation in infrastructure, DevOps, and cloud, who enjoys building solutions and taking responsibility for their operation in production.

You will join an environment where Artificial Intelligence is at the core of new products and services, working with current technologies such as LLMs, agents, RAG, Kubernetes, Docker, and real-time voice platforms.

• Design and build AI systems based on LLMs and agents, working on agent orchestration, tool use, memory, and error recovery mechanisms.

• Create agentic platforms and multi-agent systems ready for production environments, incorporating orchestration, tools, memory, sandboxing, and security.

• Design and develop real-time voice platforms using LiveKit, WebRTC/SIP, Kubernetes, and traditional voice pipelines based on STT + LLMs + TTS.

• Design RAG systems and take part in model adaptation through fine-tuning when it adds value to the domain and use case.

• Build and consume REST APIs to expose AI systems and integrate them with our data sources and internal services.

• Containerize and deploy services using Docker and Kubernetes, working with Dockerfiles, manifests, and deployments on clusters.

• Automate infrastructure through CI/CD and infrastructure as code, using tools such as Terraform and creating reproducible environments.

• Operate and monitor deployed systems through metrics, logs, and traces, designing solutions focused on observability, reliability, and scalability.

• Prototype new ideas in short sprints: hypothesis formulation, architecture definition, development, and creation of functional demos.

• Write clean, maintainable, tested, and observable code, actively participating in the team's code reviews.