Generative AI Architect

Overview

Design and implement end-to-end generative AI solution architectures that meet business requirements and adhere to industry best practices.

Responsibilities

Translate complex business challenges into scalable, secure, and cost-efficient architectures leveraging generative AI technologies.

Evaluate and select appropriate frameworks and architectural patterns to ensure performance, scalability, and maintainability.

Develop prototypes and proof-of-concepts to validate architectural decisions and demonstrate business value.

Ensure AI solutions comply with ethical standards, governance frameworks, and regulatory requirements, embedding responsible AI practices into the architecture.

Provide technical leadership and mentorship to engineering and data science teams, ensuring alignment with architectural principles.

Stay abreast of emerging trends in GenAI design patterns, agentic frameworks, orchestration tools, and MCP (Model Context Protocol) to inform strategic and architectural decisions.

Communicate complex architectural concepts to technical and non-technical stakeholders effectively.

Mandatory Skills

Generative AI architecture knowledge, including design patterns, solution blueprints, and best practices for enterprise adoption.

Expertise in cloud platforms (especially AWS) with experience designing solutions using services such as SageMaker, Bedrock, vector databases, etc.

Familiarity with MCP (Model Context Protocol) and its role in extensible, standardized agentic systems.

Strong ability to assess, select, and integrate generative AI models, orchestration frameworks, and agentic approaches into enterprise solutions.

Proven experience in solution architecture, with the ability to design system integrations, APIs, data pipelines, and deployment strategies around GenAI.

Strong stakeholder management skills, with the ability to challenge and influence diverse audiences to align on architectural direction.

Awareness of ethical considerations, governance frameworks, and responsible AI design, especially in generative use cases.

Nice to Have Skills

Experience with enterprise architecture frameworks (TOGAF, Zachman, etc.) and applying them to AI / ML solutions.

Experience

Minimum 5 years in AI / ML solution architecture, with at least 1 year focused on generative AI solution design and delivery.

Proven track record designing and delivering enterprise-grade AI systems on AWS or other major cloud platforms.

Qualifications

Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.

Relevant certifications (e.g., AWS Certified Solutions Architect, AWS Certified Machine Learning – Specialty, Google Professional Machine Learning Engineer) are a plus.

Additional information : Liderazgo técnico y capacidad de gestión de equipos; Excelentes habilidades de comunicación y presentación; Pensamiento estratégico y orientación a resultados; Capacidad para trabajar en entornos dinámicos y con múltiples interlocutores.

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