• Your primary goal is to build AI-powered infrastructure that gives VC and PE clients a genuine edge — proprietary systems that extract signals, automate decisions, and compound in value over time. • Agentic AI Systems: Multi-step LLM workflows, RAG pipelines, and agent orchestration systems — owned from architecture to production. From quick experiments to full production deployments — you know when to move fast and validate, and when to engineer for scale. Real clients depend on these systems to make investment decisions. • Full-Stack AI Applications: Client-facing web applications with AI embedded throughout — Python/FastAPI backends, React frontends, integrated with LLM providers (OpenAI, Anthropic, Gemini). Claude Code or Cursor is your primary environment. AI-assisted coding is your default mode, not a shortcut you reach for occasionally. • Data Platform Engineering: Scalable pipelines and cloud infrastructure (AWS/GCP) that underpin AI features — vector databases, data ingestion layers, API integrations. The foundation that makes everything else work. • Technical Discovery & Client Engagement: You’ll translate business needs into AI-first technical proposals — in the room with CFOs, GPs, and operating partners. You know what’s possible and you can explain it to someone who doesn’t write Python. • AI Quality & Internal Standards: Guardrails, automated testing, and observability for AI systems. You’ll help define what ‘good’ looks like across every engagement — contributing to internal engineering standards that compound over time.

• Develop quality software and simulation modules. • Automate and animate avatars and products for daily shopping activity simulation. • Optimize pipeline for large scale synthetic data rendering. • Improve data fidelity and process for AI data generation and training. • Research on advanced simulation techniques to close. • Simulation/Reality gap.

• Diseñar, desarrollar y optimizar prompts para modelos de IA generativa (LLMs). • Evaluar y mejorar continuamente la calidad de los outputs (precisión, coherencia y sesgos). • Definir casos de uso de IA en conjunto con equipos de negocio y tecnología. • Integrar soluciones de IA en flujos de trabajo y aplicaciones existentes. • Colaborar con equipos multidisciplinarios en entornos ágiles. • Documentar buenas prácticas y estándares de prompt engineering.

• Design and develop intelligent agents using Python and Autogen framework (or similar frameworks) • Collaborate with AI/ML teams to integrate LLMs (e.g., OpenAI, Anthropic, Google, etc.) into multi-agent workflows • Develop and maintain cloud-native applications and services (Azure / AWS / GCP) • Create robust APIs and integrate them with external/internal systems • Implement data ingestion, transformation, and storage logic using SQL, NoSQL, Columnar and Vector DBs • Support system scalability, performance, and maintainability using clean architecture principles • Contribute to agent orchestration logic, tool integrations, and interaction flows • Work closely with the Solutions Architect to align implementation with architecture vision • Write tests, maintain documentation, and participate in code reviews