Highlights:

Skills:Terraform, CDKTF, Kubernetes, Docker, Rust, Python, Typescript

Senior AI/ML Infrastructure Engineer 04/2022 - current
Basf, Madrid, Madrid Spain
Industry: Chemistry
• Deploy infrastructure as code to implement (AWS, AKS, and GKE) in Terraform and native SDK’s. Developing AI/ML oriented modules (OpenAI, Amazon Bedrock and VertexAI) and capabilities (Node4j, NoSQL, Kafka) based on platform requirements. • Automate open source GenAI tools (Weights Biases and NeptuneAI) and models (Llama, Gato, and GPT) in production toolkits for deployment across BASF. • Preserving the integrity of software development, CI/CD life cycle, standards, soft- ware releasing, and git source control norms, including integration and system test- ing.
In my current role at BASF, I lead the migration and integration of our enterprise infrastructure across multiple cloud platforms, including Azure, AWS, and Google Cloud. I architect and deploy scalable AI/ML solutions that power critical business operations, ensuring high performance, reliability, and security. Leveraging infrastructure-as-code practices, containerization, and advanced CI/CD pipelines, I drive cross-functional initiatives to unify our diverse cloud environments into a cohesive, robust infrastructure. My work supports innovative AI applications and analytics that deliver tangible business impact, while continuously optimizing and modernizing our technology stack for future growth.--
Machine Learning Researcher 01/2018 - 01/2022
Telecom - Ericcson, Kista / Paris, Paris Sweden
Industry: Telecommunications
Designing Deep learning strategies over historical Telecom tra?c with high security, privacy, and feasibility for Data product solutions. • Developing projects for deep learning and patent solutions over caching platforms, interconnecting enterprises such as Telecom, Ericsson, and NEC.
In this role, I spearheaded the development of deep learning models to optimize telecom network performance, with a particular focus on remote radio heads (RRHs). I designed and implemented algorithms to analyze historical traffic data, enabling predictive resource allocation and caching optimization for RRHs. This research led to the development of innovative, patentable solutions that significantly enhanced network efficiency and reduced latency. My work involved close collaboration with cross-functional teams, ensuring that the models adhered to strict security, privacy, and operational standards while driving tangible improvements in network performance.--

Polytechnic University of Catalunya 01/2019 - 12/2022
Barcelona, Barcelona, Spain
Degree: Doctoral Degree
Major:AA
In my PhD, I integrated reinforcement learning (RL), LSTM, RNN, and transformers to build an adaptive caching system capable of real-time decision-making. Reinforcement learning enabled the system to learn optimal caching policies by continuously adjusting to network traffic patterns and user demands. LSTM and RNN models captured temporal dependencies in request patterns, allowing for predictive caching that reduces latency and enhances content availability.

Resume 2025 AI/ML