Sergio García
AI Systems Developer with a background in Physics, specializing in building production-grade AI systems that combine large language models, distributed infrastructure, and real-world integrations. Experienced across MLOps, DevOps, and applied AI, with a strong focus on system design, modeling, and scalable architectures in complex environments.
Experience
AI Systems Developer (LLMs & RAG)
Design and development of production-grade AI systems leveraging Large Language Models (LLMs) to improve knowledge access and productivity in large-scale enterprise environments, supporting thousands of internal users.
- Designed and implemented RAG and agent-based systems integrating enterprise tools and data sources (via MCP and APIs)
- Built semantic retrieval pipelines using vector-based search for large-scale documentation and codebases
- Developed multi-agent architectures for task orchestration, coordination, and decomposition
- Addressed LLM context limitations by designing scalable strategies using sub-agents and context compaction
- Extended agent frameworks with custom support for handoffs and interoperability (A2A protocol)
- Implemented observability and tracing for LLM and agent-based systems in production environments
- Integrated multiple LLM providers (OpenAI, Anthropic, Gemini) across different use cases
MLOps Developer (ML Platform & Model Serving)
Development of a production-grade MLOps platform enabling reliable deployment, serving, and lifecycle management of Machine Learning models within a large-scale enterprise PaaS environment (e-commerce), supporting high-throughput, low-latency inference workloads across multiple customer-facing channels worldwide.
- Designed scalable model serving solutions supporting TensorFlow, ONNX, Scikit-learn, and other frameworks
- Implemented REST and gRPC inference endpoints compliant with V2 Inference Protocol
- Optimized deployments on OpenShift for latency, concurrency, and resource efficiency (load testing and performance tuning)
- Built CI/CD workflows for automated model promotion using GitHub Actions and Azure Machine Learning
- Implemented monitoring and observability using Prometheus and Grafana for model serving workloads
Research & Development Software Developer (MLOps)
Worked on European-funded research projects (FlexiGroBots, Grapevine), focusing on ML infrastructure, workflow orchestration, and cloud-native systems.
- Managed Kubernetes clusters and multi-user Kubeflow platform for ML experimentation
- Developed Kubeflow pipelines supporting model serving and ML lifecycle workflows
- Contributed to a Cloudify plugin for cross-platform (HPC + Cloud) workload orchestration
- Coordinated technical work across project partners and contributed to project reviews
Software Developer (DevOps)
DevOps-focused software development for 5G Core network systems, contributing to the deployment and reliability of cloud-native microservices in a telecom environment.
- Deployed and managed microservices in distributed environments using Docker and Kubernetes
- Developed and maintained CI/CD pipelines and development infrastructure
- Automated build, deployment, and release processes
- Validated integration of third-party components ensuring system stability
Research & Development Software Developer
Applied AI development across speech, computer vision, and conversational systems, focusing on real-world integration and early-stage production feasibility.
- Developed speech and speaker recognition systems using embedding-based models and performance evaluation
- Designed a custom vector similarity search layer (cosine similarity) on top of MongoDB
- Built real-time pipelines for processing large-scale telephone audio data
- Integrated speech-to-text and text-to-speech services with intent-based conversational systems (RASA)
- Developed conversational AI prototypes for public administration services
Global R&D Junior Researcher
Applied research in additive manufacturing combining computational modelling, simulation, and data infrastructure.
- Designed and operated distributed data and compute infrastructure (Hadoop and Linux clusters)
- Performed multi-scale simulations of additive manufacturing processes
- Conducted molecular dynamics studies on graphene nanocoatings for steel
- Prototyped an electromagnetic atomization system for steel powder production
- Contributed to R&D projects (~€180k), including planning and technical execution
Global R&D Intern
Supported research activities in additive manufacturing, contributing to simulation tasks and technical documentation within industrial R&D projects.
Research Intern
Carried out computational (Monte Carlo Dynamics) study of neutron-radiation damage in materials designed for future nuclear fusion reactors.