Ai cloud architect (Toluca de Lerdo)

AI, Software & Cloud Architect

Role Summary

The AI, Software & Cloud Architect is responsible for designing scalable, secure, and business-aligned technology solutions that combine artificial intelligence, modern software engineering, and cloud platforms. The role provides technical leadership across solution architecture, AI adoption, application modernization, integration design, and cloud-native delivery. This role works closely with business stakeholders, engineering teams, security, data teams, and platform owners to translate business needs into robust architecture patterns and implementation roadmaps.

Key Responsibilities

• Design end-to-end solution architectures across AI, software applications, data platforms, APIs, and cloud services.
• Define architecture standards, patterns, principles, and reusable reference designs.
• Lead the design of AI-enabled solutions, including generative AI, machine learning, automation, intelligent assistants, and analytics-driven applications.
• Architect scalable cloud-native platforms using services across AWS, Azure, GCP, or hybrid cloud environments.
• Guide application modernization, including microservices, containers, serverless, event-driven architecture, and API-led integration.
• Collaborate with data scientists, ML engineers, software engineers, and cloud engineers to move AI solutions from prototype to production.
• Ensure solutions meet non‑functional requirements including scalability, availability, performance, resilience, maintainability, and cost efficiency.
• Review technical designs and provide architecture governance throughout the delivery lifecycle.
• Support cloud migration strategies, platform selection, workload assessment, and modernization roadmaps.
• Evaluate emerging AI, software, and cloud technologies and recommend practical adoption approaches.
• Partner with security architects to ensure security, privacy, compliance, and responsible AI principles are embedded by design.
• Communicate complex technical concepts clearly to senior stakeholders, business teams, and delivery teams.

Required Skills and Experience

• Strong experience in solution architecture, software architecture, or enterprise architecture.
• Hands‑on background in software engineering using languages such as Java, Python, Java Script, Type Script, Go, or C#.
• Experience designing cloud solutions on AWS, Azure, GCP, or private cloud platforms.Strong knowledge of cloud‑native architecture, microservices, APIs, containers, Kubernetes, serverless, and Dev‑Ops practices.
• Understanding of AI/ML solution design, including model lifecycle, prompt engineering, MLOps, LLM integration, vector databases, and AI governance.
• Experience with enterprise integration patterns, event streaming, messaging, and API management.
• Strong understanding of architecture documentation, design reviews, and technical decision records.
• Knowledge of data architecture concepts such as data lakes, warehouses, lakehouses, pipelines, and real‑time analytics.
• Ability to balance strategic architecture thinking with practical implementation constraints.
• Excellent communication, stakeholder management, and technical leadership skills.

Preferred Qualifications

• Cloud certifications such as AWS Solutions Architect, Azure Solutions Architect, or Google Professional Cloud Architect.
• AI/ML certifications or practical experience with platforms such as Azure Open AI, AWS Bedrock, Google Vertex AI, Databricks, Hugging Face, or Open AI APIs.
• Experience in regulated industries such as banking, insurance.
• Experience with architecture frameworks such as TOGAF, Archi Mate, C4, or similar.
• Experience with CI/CD, infrastructure as code, Terraform, Git Hub Actions, Git Lab CI, Jenkins, or Azure Dev Ops.
• Familiarity with responsible AI, data privacy, model risk management, and AI ethics.

Key Success Measures

• High‑quality architecture designs delivered on time.
• Increased adoption of reusable architecture patterns.
• Successful delivery of AI and cloud‑native solutions into production.
• Improved scalability, resilience, security, and cost efficiency of technology platforms.
• Strong stakeholder confidence in architecture direction and technical decisions.