• Implement and deploy AI agents into internal Gatekeeper workflows. • Work directly with internal teams (Product, Marketing, Finance, Sales, Account Management, Operations, Professional Services) to understand processes and identify high-impact AI use cases. • Configure agent behaviour through prompts across different tools, rules and guardrails. • Integrate AI agents with existing systems, data sources and internal tools via APIs, webhooks and integrations. • Use Python for orchestration, data handling, light automation and evaluation. • Test, iterate and refine AI agents based on real usage and feedback. • Monitor deployed agents for accuracy, reliability and business impact, escalating issues where deeper platform changes are needed. • Collaborate with the Senior AI Platform Engineer on agent standards, tooling, observability, governance and ROI. • Document implementations and help establish repeatable internal AI deployment patterns.

• Design, implement, and manage platform architectures with a strong focus on CMC tenancy and multi-tenant scalability • Lead the development and integration of APIs, ensuring performance, security, and maintainability • Own data architecture, including logical and physical data models across systems • Plan and execute data migration strategies, including data mapping, transformation, validation, and reconciliation • Collaborate with cross-functional teams to translate business requirements into technical solutions • Establish and enforce platform engineering best practices, standards, and documentation • Monitor, troubleshoot, and optimize platform performance and reliability • Provide technical leadership and mentorship to junior and mid-level engineers • Participate in architecture reviews and contribute to long-term platform roadmaps

• Build and maintain the web app: **React dashboards**, workflow screens, configuration/admin interfaces • Deliver high-quality UX: **responsive layouts**, consistent UI patterns, accessibility basics, and performance tuning • Implement interactive data experiences: **charts, tables, filters, drilldowns, exports** • Integrate with REST APIs and external services: auth flows, request/response mapping, error handling, retries, timeouts • Contribute to API design patterns: consistency, pagination, versioning, idempotency, rate-limit awareness • Build/maintain integration-heavy workflows: webhooks, event-driven patterns, background processing (where applicable) • Support platform hardening: auditability, reliability, security posture, operational readiness • Contribute to data layer evolution and migration planning (e.g., **MongoDB → PostgreSQL**): schema thinking, migration strategy, integrity and performance considerations • Support cloud migration readiness (**AWS/Azure**): environment management, secrets, deployments, observability basics • Assist with edge/security tooling where relevant (e.g., **Cloudflare** or equivalent)

• Diseñar, desplegar y mantener infraestructura cloud para workloads de Data y Machine Learning utilizando Infrastructure as Code • Administrar y evolucionar componentes de la plataforma de datos en AWS que corren sobre Kubernetes (EKS) • Provisionar y mantener servicios como EMR on EKS, SageMaker, MWAA (Managed Airflow), Lambda, API Gateway y Step Functions • Implementar y mantener roles IAM, permisos y políticas de gobernanza alineadas con requisitos de cumplimiento • Soportar frameworks de orquestación utilizados por los equipos de Data (DBT, Airflow, Step Functions) • Colaborar con Data Engineers para resolver issues de infraestructura o plataforma que impacten pipelines • Participar en iniciativas de observabilidad de la plataforma (métricas, logging y monitoreo) • Mantener módulos de Terraform y pipelines de despliegue • Acompañar migraciones de plataforma y cambios organizacionales dentro de AWS cuando sea necesario