Position #1
January 2024 - Present
AI Director: Leading the AI Department with technical leadership, AI lifecycle governance, and strategic definition of advanced AI solutions. Directing Machine Learning and Computer Vision projects for Smart Cities and critical environments. Defining global AI system architecture and production deployment. Implementing MLOps practices at organizational level, ensuring scalability, traceability, and legal compliance. Developing agentic AI solutions, information retrieval systems, and conversational assistants for knowledge exploitation. Designing and maintaining domain ontologies across different sectors. Developing graph databases and semantic systems for knowledge representation and querying. Supervising data and model audits, and validation of compliance with current legislation. Technical liaison with clients, public administrations, universities, and technology partners. Leading multidisciplinary teams (ML, computer vision, Big Data, MLOps).
Position #2
July 2023 - Present
MLOps Engineer: Managing the complete lifecycle of AI systems, from training to production operation, ensuring traceability, reproducibility, and regulatory compliance. Designing and implementing CI/CD pipelines for AI models, including training, validation, deployment, and monitoring. Managing complete model lifecycle (data, versions, metrics, artifacts, and events). Orchestrating AI systems in cloud, on-premise, and edge environments. Defining and implementing data and model governance procedures. Auditing datasets and models, bias assessment, and data quality control. Adapting AI systems to regulatory requirements (European AI Act, ENS, applicable ISO/IEC standards). Drafting AI norms and procedures: management, orchestration, and deployment of general AI systems according to standards. Preparing technical evidence for internal and external audits.
Position #3
May 2021 - June 2023
Machine Learning Engineer: Specialist in designing, developing, and optimizing machine learning models, with main focus on computer vision applied to urban and industrial environments. Developing computer vision models for detection, classification, and behavior analysis in Smart City scenarios. Optimizing models for low-performance architectures through techniques such as pruning, quantization, distillation, and pipeline reengineering. Adapting models to specific hardware, including embedded devices, dedicated accelerators, and FPGAs. Integrating models into high-performance infrastructures (HPC) and hybrid edge-cloud environments. Designing real-time inference pipelines for distributed systems. Validating performance metrics, latency, and energy efficiency in real environments.
