Principle AI/ML Architect
Location: Remote from Spain (an indefinite Spanish employment contract)
La siguiente información tiene como objetivo proporcionar a los posibles candidatos una mejor comprensión de los requisitos para este puesto.
We are seeking a highly experienced and hands-on Principal AI/ML Architect & Applied AI Lead to drive the design, development, and operationalization of enterprise-scale AI systems across research and production environments.
This role combines deep technical expertise in Machine Learning, Generative AI, distributed data systems, and cloud-native architectures with strategic leadership capabilities. The ideal candidate will lead complex AI initiatives end-to-end — from experimentation and research to scalable deployment in global enterprise environments.
The position requires a strong balance between:
- technical leadership,
- hands-on implementation,
- AI strategy,
- cross-functional collaboration,
- and mentoring of engineering and data science teams.
Requirements:
- Master's or Ph.D. in Computer Science, Data Science, Machine Learning, or a related field
- 10+ years of experience in AI/ML, data science, or distributed systems engineering.
- Proven experience designing and deploying production-grade AI solutions at enterprise scale.
- Strong background in both research and industrial AI environments.
- Experience leading global or distributed technical teams.
- Demonstrated success delivering AI transformation initiatives.
- Large Language Models (LLMs)
- Generative AI systems
- NLP / NLU
- Apache Spark
- Databricks
- Delta Lake
- SQL / NoSQL databases
- Distributed computing architectures
- Streaming and batch processing pipelines
- Azure and/or AWS
- Docker
- Kubernetes
- CI/CD pipelines
- Infrastructure-as-Code
- MLOps frameworks
- Python
- Scala
- Experience with AI governance and responsible AI practices.
- Experience building AI platforms serving multiple teams or business units.
- Experience optimizing cloud infrastructure and reducing operational costs.
Responsibilities:
- Lead the design and implementation of AI/ML solutions across multiple business domains.
- Drive enterprise adoption of Large Language Models (LLMs), Generative AI, NLP/NLU, and advanced analytics solutions.
- Define AI architecture standards, MLOps best practices, and scalable deployment strategies.
- Evaluate emerging AI technologies and identify opportunities for innovation and operational impact.
- Translate research initiatives into production-ready AI solutions.
- Architect scalable distributed data-processing systems capable of handling large-scale datasets and real-time pipelines.
- Design and optimize cloud-native AI platforms using modern data engineering frameworks.
- Lead cloud migration and modernization initiatives from on-premises environments to Azure and/or AWS.
- Implement efficient data pipelines leveraging Spark, Delta Lake, Databricks, Kubernetes, and containerized environments.
- Ensure reliability, scalability, observability, and cost-efficiency of AI infrastructure.
- Design and implement enterprise-grade chatbot and conversational AI platforms.
- Lead development of Retrieval-Augmented Generation (RAG), agentic workflows, and LLM orchestration systems.
- Define governance, evaluation, and monitoring strategies for GenAI systems.
- Collaborate with research teams to operationalize LLM-based applications securely and responsibly.
- Lead cross-functional teams composed of data scientists, ML engineers, software engineers, and business stakeholders.
- Mentor engineers and researchers in AI/ML best practices, architecture, and software engineering standards.
- Coordinate general AI initiatives across distributed teams and multiple geographies.
- Communicate technical concepts effectively to executive and non-technical audiences. xcskxlj
- Support innovation programs and AI adoption strategies across the organization.