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Est. Monthly
Estimated €5,500 - €6,917
Posted August 6, 2026 · 2 days agoLast seen August 7, 2026Est. expiry September 10, 2026

AI Engineer

Staff AI Engineer
Barcelona, Spain
Remote · Software Engineering
Full-time · Expert
English
No People Management
Bachelor
7 years experience
How this salary compares
Salary Context: AI Engineer

Hover or tap a row for full statistics (EUR / month on this chart).

Salary analysis

Compared with the selected benchmark ("All roles in Barcelona, Spain"), this listing's salary midpoint is about 91% lower. The offer sits below the benchmark range (€3,900–€8,050). The listed pay band (€458–€576) is tighter than the benchmark, which suggests lower salary variability. This benchmark is based on 2 comparable listings.

Monthly salary comparison for AI Engineer
MarketLower bound (25th percentile)MedianUpper bound (75th percentile)
Market Average: AI Engineer€10,128/per month€12,298/per month€14,468/per month
All roles in Barcelona, Spain€3,900/per month€6,209/per month€8,050/per month
Pay in our data — not quoted in ad (Expert)€458/per month€517/per month€576/per month
About the role

We are looking for a Staff AI Engineer to play a key role in building the core of our AI platform. In this position, you will design and develop production-grade systems that power intelligent automation, agentic workflows, and large-scale retrieval services. This is a highly technical, hands-on role that involves close collaboration with product and platform teams to transform advanced AI concepts into reliable, scalable, and secure solutions used across our enterprise ecosystem. You will also be responsible to: - Design, build, and maintain AI-powered services and APIs, leveraging LLMs (OpenAI, Anthropic, Qwen, OSS models) and custom ML models. - Develop an enterprise-grade agentic framework that enables orchestration, retrieval, and collaboration between multiple AI agents. - Implement and optimize knowledge retrieval systems and agentic search capabilities using vector databases such as Qdrant and ElasticSearch. - Write well-structured, efficient, and testable Python code for production services, experimentation, and internal developer tools. - Build and maintain shared Python libraries and SDKs used across multiple applications and microservices. - Collaborate with cross-functional teams on architecture, internal protocols, and API standards to ensure consistency and reliability across the platform. - Develop and enhance monitoring, validation, and observability for production-grade AI solutions. - Drive the full software development lifecycle - from design and implementation to deployment, monitoring, and continuous improvement. - Identify and resolve performance bottlenecks, reliability issues, and scaling challenges in complex, data-intensive environments. - Participate in code reviews and technical discussions, mentoring other engineers and contributing to a culture of excellence. Example Projects: - Building an evaluation and observability framework for AI model performance and reliability. - Developing an agentic orchestration platform that enables collaboration among multiple AI agents and tools. - Implementing semantic retrieval and agentic search capabilities over large enterprise knowledge bases. - Designing AI services that process and reason over high-volume real-world data at scale. Requirements: - Bachelors or Masters degree in Computer Science, Engineering, or a related field, or equivalent practical experience. - 7+ years of experience as a Software Engineer, with strong proficiency in Python. - Proven track record of building and maintaining production-grade systems using Python. - Strong understanding of distributed systems, API design, and data-driven architectures. - Experience with relational and non-relational databases (PostgreSQL, Elastic, Qdrant, or similar). - Familiarity with AI/ML system design, including LLM integration and evaluation pipelines. - Knowledge of DevOps and observability practices (CI/CD, monitoring, metrics, and model validation). - Python  FastAPI  LLM APIs (OpenAI, Anthropic, Qwen, OSS)  LiteLLM  Qdrant  PostgreSQL  ElasticSearch  Langfuse  Kubernetes  GitHub Actions  ArgoCD Preferred Skills: - Experience working with multiple LLM providers (OpenAI, Anthropic, Qwen, open-source models). - Background in developer platforms or AI infrastructure services. - Familiarity with vector databases, semantic retrieval, and knowledge graph architectures. - Exposure to Langfuse, LiteLLM, LangChain, or similar frameworks. - Experience developing enterprise-scale SaaS or distributed backend systems. - Contributions to open-source projects in Python, AI, or infrastructure engineering.

Job Details

Responsibilities

  • Design, build, and maintain AI-powered services and APIs using LLMs and custom ML models
  • Develop an enterprise-grade agentic framework for orchestration, retrieval, and collaboration between AI agents
  • Implement and optimize knowledge retrieval systems and agentic search using vector databases like Qdrant and ElasticSearch
  • Write efficient and testable Python code for production services and internal tools
  • Build and maintain shared Python libraries and SDKs
  • Collaborate on architecture, internal protocols, and API standards
  • Develop monitoring, validation, and observability for AI solutions
  • Drive the full software development lifecycle from design to deployment
  • Resolve performance bottlenecks and scaling challenges in data-intensive environments
  • Mentor other engineers and participate in code reviews

Requirements

  • Bachelors or Masters degree in Computer Science, Engineering, or a related field, or equivalent practical experience
  • 7+ years of experience as a Software Engineer
  • Strong proficiency in Python
  • Proven track record of building and maintaining production-grade systems using Python
  • Strong understanding of distributed systems, API design, and data-driven architectures
  • Experience with relational and non-relational databases (PostgreSQL, Elastic, Qdrant, or similar)
  • Familiarity with AI/ML system design, including LLM integration and evaluation pipelines
  • Knowledge of DevOps and observability practices (CI/CD, monitoring, metrics, and model validation)

Skills & Technologies

PythonFastAPILLM APIsOpenAIAnthropicQwenLiteLLMQdrantPostgreSQLElasticSearchLangfuseKubernetesGitHub ActionsArgoCD

Education Level

Bachelor
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