Senior Software Engineer - AI Agents
Multiverse Computing3 days ago
Zaragoza, SpainSenior
Responsibilities
- Own the technical design and implementation of AI agent systems from initial architecture through production operation.
- Build backend services, APIs, adapters, and connectors integrating language models, enterprise data, external tools, databases, and customer systems.
- Design agent workflows involving tool use, routing, state management, memory, retrieval, human-in-the-loop steps, and controlled multi-step execution.
- Develop RAG pipelines, SQL-based agents, web search, reranking, citation, context management, and other knowledge-grounding capabilities.
- Define evaluation strategies using representative datasets, golden tests, failure taxonomies, regression testing, human review, and production feedback.
- Build reliable and observable execution flows with validation, retries, fallbacks, tracing, structured logging, metrics, alerting, and incident analysis.
- Deploy and operate AI systems using containers, CI/CD, model endpoints, data stores, cloud infrastructure, and customer environments.
- Improve latency, scalability, and cost through caching, concurrency control, asynchronous execution, streaming, model selection, and efficient context management.
- Implement permissions, input and output validation, sensitive-data handling, auditability, safety controls, and human approval processes.
- Build reusable components, templates, and reference implementations for shared platforms and product capabilities.
- Collaborate with customers, ML engineers, data engineers, DevOps engineers, software engineers, and product teams.
- Contribute through design documents, code reviews, engineering standards, mentoring, and technical guidance.
Requirements
- Master’s degree in Computer Science, Software Engineering, or a related technical field, with relevant practical experience.
- At least 5 years of professional Software Engineering experience, including significant experience designing and owning backend or AI/ML systems in production.
- Hands-on experience building and deploying LLM-powered applications or AI agent systems beyond the prototype stage.
- Strong proficiency in Python and software engineering fundamentals including modular design, testing, version control, code review, CI/CD, and maintainability.
- Experience designing APIs and integrating external services, tools, databases, and data pipelines into production software.
- Practical understanding of agentic system design, including tool use, structured outputs, workflow orchestration, state management, retrieval, error handling, and fallback strategies.
- Experience implementing evaluation approaches for LLM or agent systems and using results to improve behavior.
- Experience with production reliability and observability practices including structured logging, tracing, metrics, alerting, and incident analysis.
- Experience deploying and operating services with cloud infrastructure, containers, and production data stores.
- Strong system-design skills and ability to explain architectural trade-offs involving quality, latency, reliability, scalability, and cost.
- Ability to own complex work, collaborate across disciplines, and communicate with technical and non-technical stakeholders.
- Fluent written and spoken Spanish and English.
- Hands-on experience with agent frameworks or SDKs such as LangGraph or LangChain.
- Experience building end-to-end RAG systems with ingestion, retrieval, vector databases, reranking, grounding, citation, and evaluation.
- Experience with SQL agents, web-search agents, Model Context Protocol integrations, or other tool-using AI systems.
- Experience with FastAPI, PostgreSQL, vector databases, message queues, Docker, Kubernetes, AWS, Azure, or GCP.
- Familiarity with LLM inference and serving platforms, model routing, structured generation, and hosted, open-source, and optimized model trade-offs.
- Experience converting project-specific implementations into configurable reusable components or shared platform capabilities.
- Experience mentoring engineers and improving engineering quality through code reviews, design reviews, and technical guidance.
Benefits
- Indefinite contract, variable performance bonus, signing bonus, private health insurance, flexible remuneration, educational budget eligibility, and relocation support where applicable.
- Work visa sponsorship is available if applicable.
- Hybrid work opportunity with flexible working hours.
- Language classes and discounted lunch options.
- Career plan, learning and teaching opportunities, and an inclusive progressive company culture.
Tech Stack
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