• Diseñar arquitecturas y buenas prácticas agénticas para IA Generativa en Microsoft Copilot • Definir estrategias de adopción de IA en entornos empresariales • Interlocución con C-Level para requisitos y definición estratégica • Comunicación fluida en inglés

• Lead the design and development of AI-driven solutions for supply chain optimization. • Build and orchestrate agentic AI systems to automate decision-making processes. • Develop and integrate machine learning models for predictive analytics. • Collaborate with cross-functional teams to translate business needs into technical solutions. • Design user-facing applications using React with a strong focus on UX/UI. • Ensure scalability, performance, and reliability of AI systems. • Drive technical decisions and best practices across the AI engineering team. • Mentor and guide engineers, promoting a high-performance engineering culture.

• Build RAG pipelines and agentic workflows using LangChain and LangGraph • Develop knowledge base chatbots with semantic search and conversation memory • Implement advanced retrieval strategies (Hybrid Search, HyDE, chunking) • Design multi-agent architectures (Agentic RAG, Self-RAG, Corrective RAG) • Integrate LLMs (Claude, GPT-4o, Llama, Mistral) via APIs and hosting platforms • Work with vector databases (Pinecone, Weaviate, PGVector) • Build solutions using AWS Bedrock, Agents, and Knowledge Bases • Set up evaluation (Ragas) and observability (LangSmith/Phoenix) • Deploy AI services using FastAPI, Docker, and OpenTelemetry

• Write and ship production AI code daily — you are an active contributor, not just an architect. • Architect agentic AI workflows using LangGraph — stateful, multi-agent workflows built for enterprise scale and reliability. • Own AI observability via LangFuse: tracing, prompt versioning, evaluation, and performance benchmarking across all model interactions. • Set AI engineering standards for agent design patterns, RAG, prompt management, context optimization, and tool-calling strategies. • Partner with product and platform teams to deliver AI architectures that meet enterprise SLA, security, and compliance requirements. • Evaluate and adopt emerging tooling— benchmarking LLM providers, orchestration frameworks, and agentic stack improvements. • Mentor engineers as a natural extension of your work — sharing knowledge through code reviews, pairing sessions, and design discussions, not through management overhead. • Represent Acquia's AI capabilities in customer architectural reviews, technical discovery, and roadmap conversations.