Overview

In this role you will design and scale intelligent data platforms that enable multi-agent AI collaboration to solve analytical and mathematical tasks. You will oversee the integration of LLMs, symbolic solvers, and data pipelines into production systems, balancing performance, cost, and governance. You’ll lead engineering work, mentor teammates, and translate business problems into AI-driven solutions for aviation operations. This is a chance to shape AI-enabled data platforms that improve efficiency and collaboration across the air transport ecosystem.

Compensaciones / Incentivos

- Flex Week: work from home up to 2 days/week
- Flex Day
- Flex-Location: up to 30 days/year remote work
- Employee Wellbeing: EAP for you and dependents 24/7
- Champion Health – wellbeing platform
- LinkedIn Learning and professional development

Responsabilidades

- Design and scale intelligent data platforms with multi-agent collaboration
- Build multi-agent architectures for planning, memory, tool usage and coordination
- Create orchestration layers with guardrails for robustness, traceability, and observability of agent decisions
- Integrate mathematical engines and external tools into LLM-driven workflows
- Develop scalable batch and real-time data pipelines and data models supporting agent memory and retrieval (vector databases/RAG)
- Establish LLMOps/MLOps practices for deployment, monitoring, evaluation and testing (including mathematical correctness)




- Optimize performance and cost of LLM and compute-heavy workloads; drive CI/CD and operational excellence
- Provide technical leadership: set best practices, mentor engineers, translate business problems into AI-driven solutions

Requisitos principales

- 6+ years in data engineering and/or distributed systems with hands-on delivery of production platforms
- Strong Python and SQL skills; experience with data models, data quality controls and observability
- Experience with distributed data processing and streaming (e.g., Spark, Kafka) and cloud platforms (AWS, Azure, or GCP)
- Experience building LLM-based systems; familiarity with agent frameworks (orchestration, tool usage, memory) and RAG/vector databases
- Strong mathematical foundation (linear algebra, probability/statistics, optimization) and ability to translate business problems into formal models
- Experience with scientific computing and/or solver integration (e.g., NumPy/SciPy/SymPy, optimization libraries, symbolic/numerical engines)
- Understanding of security best practices and governance for data/AI platforms (IAM, secrets management, auditability)
- Proven system design capability and technical leadership (architecture decisions, standards, mentoring)
- Aviation/OCC domain exposure is a strong advantage

- leadership and mentoring
- problem solving and analytical thinking
- collaboration with cross-functional teams
- Python
- SQL
- Spark

📌 Data Scientist - Analytics and ML (Barcelona)
🏢 Sita - Société Internationale De Télécommunications
📍 Barcelona