• Use AI coding agents (Claude Code, OpenAI Codex, OpenCode, or similar) to rapidly build and iterate on production applications. • Automate business workflows and processes by building custom tools, scripts, and integrations that reduce manual work. • Develop LLM-based applications including document processing, intelligent assistants, and data extraction workflows. • Create proof-of-concepts to demonstrate feasibility and business value of AI initiatives. • Deploy AI applications to production and support real end users. • Monitor usage, gather feedback, and iterate. • Translate business needs into technical requirements by observing how people actually work, not just what they ask for. • Communicate progress and tradeoffs clearly to non-technical stakeholders. • Ensure AI solutions meet security, privacy, and compliance requirements, which is particularly important given healthcare data. • Monitor and maintain deployed solutions; iterate based on user feedback. Manage costs and provide input on AI tools and platform selection.

• Design, build, and deploy production-grade AI systems • Include LLM-powered conversational agents, RAG pipelines, NLP workflows, and voice AI integrations • Deliver intelligent, reliable, and measurable AI solutions for enterprise clients across various sectors • Help clients automate customer interactions at scale with high accuracy, low latency, and strong business impact.

• Collect, evaluate, and prioritize feature requests from users, sales, and market insights. • Conduct market research and benchmark competitors, focusing on API platforms, AI technologies (LLMs, RAG, vector search), and user experience in trust-focused products. • Develop detailed user stories and acceptance criteria for APIs (REST/GraphQL), SDKs, webhooks, and AI-powered functionalities. • Collaborate with the VP of Product and engineering leads to prioritize features in line with the overall product strategy. • Plan and maintain the product roadmap, adjusting priorities based on market trends and feedback. • Manage a structured product backlog with clear priorities. • Oversee feature releases following CI/CD practices. • Lead internal testing sessions to validate usability, accessibility, and quality. • Analyze feature performance using tools like Mixpanel and recommend improvements. • Keep product documentation current, including API references, wireframes, and release notes. • Work alongside marketing and customer success teams to ensure smooth product launches and communications.

• Arquitectar y construir sistemas de IA agentica utilizando frameworks como LangChain, AutoGen, DSPy, CrewAI o herramientas de orquestación desarrolladas a medida. • Diseñar flujos de trabajo modulares y orientados a objetivos que permitan a los agentes autónomos planificar tareas, colaborar con otros agentes e interactuar con herramientas externas. • Implementar patrones de agentes como planner–executor, ReAct, reflection y self‑verification. • Desarrollar estrategias de prompting avanzadas, incluyendo chain-of-thought, tree-of-thought, salidas estructuradas y versionado de prompts. • Construir e integrar sistemas de memoria (corto plazo, largo plazo, episódica) para soportar persistencia del contexto y capacidades de aprendizaje. • Integrar LLMs (OpenAI, Anthropic, Mistral, Cohere, DeepSeek, modelos de código abierto) con entornos de ejecución como Python, Bash, SQL, APIs REST y herramientas de automatización de navegador. • Implementar tool calling/funciones, orquestación de APIs y agentes de ejecución de código. • Diseñar y optimizar pipelines de RAG utilizando bases de datos vectoriales y búsqueda híbrida. • Optimizar el rendimiento de agentes para baja latencia, fiabilidad, eficiencia de costes y escalabilidad en aplicaciones reales como agentes de investigación, copilotos de programación o asistentes de planificación. • Desplegar y mantener sistemas agenticos en producción utilizando Docker, pipelines de CI/CD e infraestructura en la nube. • Monitorizar el comportamiento de los agentes, registrar trazas de ejecución y pasos de razonamiento (cuando corresponda) y mejorar continuamente la robustez y alineación del sistema. • Colaborar estrechamente con científicos de datos, ingenieros de software, equipos de producto y diseñadores UX para entregar soluciones de IA de alto impacto. • Implementar flujos de trabajo con humanos en el circuito (HITL) para supervisión, retroalimentación y autonomía segura.