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Build and deploy GenAI solutions (RAG, agents, assistants) for enterprise clients, turning AI challenges into measurable results in weeks while balancing speed, cost, and governance.
Build and deploy generative AI agents, RAG systems, and ML models end-to-end using Python, LangChain, and Azure OpenAI to drive healthcare analytics and business impact.
Build and scale data-driven and generative AI solutions for global clients in banking, pharma, and public sector using cloud platforms, Spark, and Azure OpenAI.
Build and deploy agentic AI systems and LLM-powered applications for a global insurer, focusing on RAG, multi-agent orchestration, and robust MLOps pipelines.
Build and maintain data pipelines, clean and structure financial data, and integrate GenAI solutions like LLMs and RAG for a banking-focused project using Python, SQL, and ETL/ELT.
Design and build autonomous AI agents and multi-agent systems using RAG architectures with vector databases, LangChain, and LangGraph to automate business processes and integrate with CRM, media, and analytics platforms.
Build and maintain data pipelines in AWS (S3, Glue, Aurora, Neptune) to feed AI/ML applications, including RAG and semantic search, using Python and SQL.
Build and maintain data pipelines in AWS (S3, Glue, Aurora, Neptune) to feed AI/ML applications, using Python, SQL, and graph queries for RAG and semantic search solutions.
Builds AI agents and RAG systems to automate marketing workflows, integrate APIs via MCP, and deliver generative insights for ad-tech and media clients.
Build autonomous AI agents and RAG systems to automate marketing workflows, integrating LLMs, vector databases, and cloud platforms like GCP/AWS.
Designs and maintains AI-augmented data extraction pipelines using Pydantic, Scrapy, and LLM agents to autonomously scrape and validate structured data from websites while ensuring compliance and reusability.
Build and run the AIOps platform that keeps AI models, LLM pipelines, and agents reliable, scalable, and cost-efficient in AWS/Azure.
Lead AI-driven full-stack projects, integrating generative AI agents and platforms using Python/TypeScript, LangChain, and Docker in a hybrid Madrid-based role.
Build and deploy AI-powered full-stack apps using Angular, Docker, and LLMs; optimize models and engineer prompts to solve real client problems.
Build AI-powered enterprise solutions for pharma using LLMs, RAG, and knowledge graphs to create intelligent decision-support tools and synthetic personas.
Build a full-stack Agentic AI platform for Life Sciences, integrating autonomous agents, RAG, and LLMs with Python, FastAPI, React, and Kubernetes.
Build and scale AI-driven products using LLMs, RAG pipelines, and vector databases with Rust or Golang, from prototype to production in rapid cycles.
Build an AI-powered employment platform as a full-stack engineer, coding Python/Django backends and React UIs while shaping product direction and mentoring teammates.
Lead a full-stack team building AI-powered products using Python, Node.js, React, and cloud services (AWS/Azure), with a focus on RAG systems, LLMs, and LangChain.
DevOps Engineer to design, deploy, and maintain AI infrastructure using Kubernetes, Docker, and LLMOps tools like Ollama and LangChain, ensuring scalable, secure systems for logistics and industrial clients.
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