• You will lead the development and deployment of scalable, efficient AI models, optimizing performance, scalability, and cost. • You will work closely with data scientists, software engineers, and product managers to integrate AI solutions into larger systems and ensure they are robust, functional, and scalable. • You will analyze large volumes of complex data and develop advanced predictive models that effectively solve client-specific problems. • You will be responsible for the continuous improvement of existing systems, implementing strategies to optimize performance, efficiency, and reliability. • You will integrate AI solutions with cloud platforms (AWS, Google Cloud, Azure), leveraging their capabilities to improve performance and reduce costs. • You will maintain an innovative mindset, researching the latest trends in AI, machine learning, and emerging technologies to apply to current and future projects. • You will document complex technical processes and communicate results and innovations to both technical and non-technical stakeholders.

• Diseñar, implementar y mantener soluciones empresariales complejas basadas en Inteligencia Artificial Generativa, Large Language Models (LLM) y Retrieval-Augmented Generation (RAG). • Ser el guardián de la calidad y robustez de las soluciones GenAI, liderando la implementación de la metodología estandarizada para entregar valor real de negocio. • Trabajar directamente con clientes enterprise para traducir problemas de negocio en arquitecturas GenAI escalables y confiables. • Tomar decisiones críticas sobre el enfoque técnico, selección de modelos, y estrategias de retrieval. • Construir pipelines completos de RAG, optimizando cada componente para que maximice la relevancia y performance. • Implementar guardrails robustos para control de alucinaciones; diseñar e implementar pipelines de ingesta, procesamiento y estructuración de fuentes de conocimiento. • Implementar frameworks de evaluación exhaustivos combinando métricas automáticas, benchmarks, y evaluación humana. • Integrar con modelos como GPT-4, gestionando deployments en plataformas cloud y optimizando para latencia y costo. • Implementar agentes GenAI capaces de razonamiento y ejecución de acciones mediante tool integration. • Configurar alertas, dashboards, y feedback loops para mejora continua.

• Build AI Agents with the domain context for specialised tasks • Integrate the agents with the applications • Build, train, and deploy gen AI • Perform feature engineering, model validation, and experiments • Implement agents in production environments (APIs, microservices, cloud architecture) • Document your work, follow best MLOps practices, and help shape AI development standards at Appsilon • Work closely with Data Engineers, Developers, and Analysts to integrate Gen AIML solutions into full-scale applications

• 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.