• Design, develop, and implement machine learning models and algorithms • Analyze large datasets and extract meaningful insights • Collaborate with cross-functional teams to integrate ML solutions into existing systems • Optimize ML models for performance and scalability • Stay current with the latest advancements in machine learning and AI • Create and implement big data processing pipelines and architectures • Design and build scalable data infrastructure to support ML applications

• Automatizar mediante MLOps la creación de modelos en la plataforma analítica. • Implementar pipelines CI/CD para integración y entrega continuas de código y modelos. • Configurar el proceso gobernado desde Azure DevOps e integrar con otros servicios.

• Design and implement scalable systems for serving deep learning and reinforcement learning models. • Optimize inference performance of deep learning and reinforcement learning models using techniques such as quantization, pruning, and distillation. • Utilize GPU computing to accelerate model training and inference. • Develop and deploy production workflows for training and serving machine learning models. • Collaborate with data scientists and software engineers to design and implement machine learning systems. • Monitor and improve the performance of machine learning models in production. • Stay up-to-date with the latest research and techniques in deep learning and reinforcement learning.

• Design, build, and deploy production AI agents and multi-agent orchestration systems with prompt engineering, LLM chaining, and tool-calling patterns for complex, multi-step workflows. • Architect RAG pipelines with vector search, hybrid retrieval, and knowledge base management for AI-driven question-answering and decision-support. • Integrate third-party AI platforms and LLM providers, designing authentication flows, tool schemas, and agent-to-backend communication. • Design AI agent security architectures including token exchange, delegated access, and user verification flows for systems acting on behalf of users. • Build production microservices and APIs (FastAPI, Flask, Node.js) serving as orchestration layers and tool endpoints for AI agent systems. • Architect authentication and authorization for AI services: identity provider integration, token validation, and service-to-service auth. • Deploy, monitor, and maintain ML models and AI agent endpoints on cloud platforms (Databricks, AWS SageMaker) including scaling and health management. • Build data ETL pipelines for feature engineering, transaction processing, and knowledge base ingestion. • Develop evaluation and monitoring frameworks for non-deterministic AI systems: agent correctness testing, retrieval quality, and alerting. • Author technical design docs, architecture diagrams, and API contracts; mentor junior and mid-level engineers on AI development practices. • Lead architecture reviews and produce design documents with implementation roadmaps; evaluate emerging AI technologies to inform team strategy. • Collaborate cross-functionally with product, platform, security, and operations to define requirements, prioritize features, and ship AI integrations end-to-end.