Technical AI Architect
Alongside
Posted: April 10, 2026
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Quick Summary
A Technical AI Architect will be responsible for designing and implementing AI solutions, ensuring scalability, security, and adherence to enterprise architecture principles and corporate security, risk, and governance standards.
Required Skills
Job Description
Alongside partners with organizations to drive digital transformation and build high-performing tech teams. We are recruiting a highly skilled Technical AI Architect to lead the design of robust, scalable, and secure solutions in the domains of Data and Artificial Intelligence. The role ensures full alignment with business needs, enterprise architecture principles, and corporate security, risk, and governance standards.
This professional will play a key role in defining end-to-end architectures for AI platforms, products, and use cases, ensuring seamless integration within the corporate technology ecosystem while promoting engineering best practices and long-term solution sustainability.
Responsabilities:
• Design and lead end-to-end AI architectures in collaboration with business and technical teams
• Translate business requirements into scalable, secure, and resilient technical solutions
• Define and integrate AI systems across data, infrastructure, and application layers
• Ensure compliance with security, privacy, and resilience-by-design principles
• Stay aligned with emerging Data, AI, and GenAI trends and technologies
Requirements:
• +5 years in Technical Architecture roles, with AI exposure
• Experience designing enterprise-scale and cloud/hybrid architectures (Azure, AWS, private cloud)
• Strong knowledge of distributed systems, APIs, microservices and modern engineering practices (CI/CD, IaC, observability)
• Experience with legacy modernization and integration
• Strong programming skills (e.g. Python, Java, SQL, JavaScript)
• Fluent English (Spanish is a plus) and degree in a relevant field
AI / GenAI Expertise
• Experience designing enterprise AI platforms and solutions
• Strong knowledge of MLOps/LLMOps (lifecycle, deployment, monitoring, drift, versioning)
• Experience with RAG, embeddings, vector databases and semantic search
• Knowledge of LLM integration, prompt engineering, guardrails and AI observability
• Understanding of AI governance, security and responsible AI principles
• Experience integrating enterprise AI services (e.g. Azure OpenAI)
• Knowledge of secure architecture (identity, access, secrets management)
Benefits:
• Employment Contract
• Health Insurance
• Meal Card
• Gym Ticket
• Hybrid work