• Diseñar, desarrollar y desplegar servicios de integración y APIs • Gestionar el ciclo de vida de las APIs: seguridad, versionamiento y monitoreo • Implementar estándares y buenas prácticas de integración • Atender incidentes y ejecutar análisis de causa raíz • Ejecutar despliegues y configuraciones en ambientes de desarrollo, pruebas y producción • Elaborar documentación técnica y diagramas de arquitectura

• Define AI feature specifications upfront — including acceptance criteria, evaluation metrics, prompt contracts, and expected behaviors — and champion this spec-driven approach across the team. • Own end-to-end AI feature delivery across the full AI SDLC: spec definition, prototyping, development, evaluation, deployment, and production monitoring. • Build production-grade LLM and agentic AI applications using Spring AI — including RAG pipelines, agent orchestration, tool-use patterns, guardrails, and human-in-the-loop workflows. • Architect and operate AWS AI infrastructure (Bedrock, Bedrock Agents, Agent Core, SageMaker) alongside core AWS services (ECS/EKS, Lambda, S3, DynamoDB, RDS, API Gateway). • Design and implement scalable microservices and distributed systems in Java, TypeScript, and Python that power the Archer AI platform. • Build CI/CD pipelines for AI workloads — including LLM evaluation pipelines and automated regression testing for AI outputs — using Terraform, CloudFormation, Docker, Kubernetes, and GitHub Actions. • Drive AI-specific operational practices: observability, drift detection, quality scoring, feedback loops, and incident response for non-deterministic systems. • Communicate technical concepts clearly to both technical and non-technical stakeholders; author AI specs, design documents, and architectural decision records. • Mentor engineers, conduct thorough code reviews, and champion engineering excellence.

• Lead the architecture and design of complex, large-scale distributed systems • Design, develop, and maintain scalable, fault-tolerant microservices for both real-time and batch data processing • Build and maintain high-performance REST APIs with a focus on scalability, reliability, and maintainability • Design and implement data models and queries for efficient storage and retrieval across SQL and NoSQL databases • Lead data quality initiatives to ensure consistency, accuracy, and reliability of data across the platform • Collaborate with cross-functional teams to translate platform requirements into effective technical solutions • Drive technical standards and best practices for code quality, testing, and observability across engineering teams • Mentor and provide technical guidance to engineers through code reviews, design discussions, and direct support • Take end-to-end ownership of the software development lifecycle, including deployment, monitoring, and incident response

• Lead the design, development, integration, deployment, and ongoing support of the organization's AI platform and related engineering capabilities • Define and implement platform architecture, engineering standards, and development practices • Partner with product, data, infrastructure, security, and business teams to align platform capabilities with enterprise needs • Oversee engineering teams in building reusable platform services, tools, and components • Drive modernization and integration efforts to connect existing systems and processes with AI platform capabilities • Monitor platform performance and operational health • Establish effective operating models, support processes, and governance practices • Communicate platform priorities, delivery progress, technical risks, and investment needs to senior leaders