AI Solution Architect

CI&T

. Design end-to-end AI solutions including Generative AI applications, LLM-powered workflows, intelligent agents, RAG solutions, virtual assistants and intelligent automation .

Posted 10/21/2026full-timeRemote • ColombiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and implementing end-to-end AI solutions, including Generative AI applications and LLM-powered workflows, while ensuring compliance with Responsible AI principles. Proficient in collaborating with multidisciplinary teams to translate business requirements into scalable technical architectures and integrating AI solutions with enterprise applications.

Highest-signal resume keywords

Generative AI Solutions DesignLLM ProficiencyMicrosoft Azure ExperiencePython ProgrammingAI Security and Compliance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI/ML Solution DevelopmentPrompt EngineeringAPI Design and IntegrationMicroservices ArchitectureCloud-Native ApplicationsVersion ControlCI/CD PracticesDevOps MethodologiesRAG ImplementationVector Search

Soft Skills

Analytical SkillsCommunication SkillsCollaboration Skills

Tools & Technologies

Azure AI FoundryAWS BedrockLangChainSemantic KernelKubernetesSnowflakeContainersLLMOps

Certifications & Qualifications

Cloud CertificationsAI Certifications

Industry Keywords

HealthcareLife SciencesMedTechFinancial ServicesRegulated Industries

Tech Stack

Tools & technologies

AWSAzureCloudKubernetesMicroservicesPython

About the role

Key responsibilities & impact

Design end-to-end AI solutions including Generative AI applications, LLM-powered workflows, intelligent agents, RAG solutions, virtual assistants and intelligent automation
Translate business requirements into technical architectures, selecting models, services, data sources, integrations and infrastructure
Build and validate prototypes and proofs of concept
Collaborate with AI engineers, software engineers, data scientists and data engineers to move solutions from prototype to production
Design agentic solutions involving tool calling, orchestration, memory, retrieval and human-in-the-loop patterns
Integrate AI solutions with enterprise applications, APIs, data platforms, vector databases and cloud services
Build reusable AI components, services, APIs and architectural patterns
Evaluate and recommend AI models, frameworks and platforms based on performance, scalability, security and cost
Embed Responsible AI, security, privacy and compliance into designs with relevant enterprise teams
Help establish evaluation, monitoring, observability and LLMOps practices
Document architectures, technical decisions, integration patterns and implementation guidance
Track developments in Generative AI, agentic AI and cloud AI platforms and identify meaningful applications

Requirements

What you’ll need

Bachelor’s degree in Computer Science, Software Engineering, Systems Engineering, Data Science, AI or a related field, or equivalent experience
6+ years in software engineering, solution or cloud engineering, data/AI engineering or solution architecture
Hands-on experience designing and building AI/ML or Generative AI solutions
Solid working knowledge of LLMs, prompt engineering, RAG, embeddings, vector search and AI agents
Experience with Microsoft Azure and/or AWS
Experience designing and integrating APIs, microservices and cloud-native applications
Strong Python skills and ability to build prototypes and reference implementations
Good command of version control, testing, CI/CD and DevOps
Awareness of AI security, data privacy, Responsible AI and governance in enterprise settings
Strong analytical skills and ability to explain technical trade-offs to technical and non-technical audiences
Experience working with multidisciplinary, distributed teams
Fluent English (written and spoken) and Spanish
Portuguese is a plus
Nice-to-have experience with Azure AI Foundry/Azure OpenAI, AWS Bedrock or similar enterprise GenAI platforms
Nice-to-have experience with LangGraph, LangChain, Semantic Kernel, AutoGen or CrewAI
Nice-to-have end-to-end RAG implementation experience
Nice-to-have Azure AI Search or other vector database/enterprise search experience
Nice-to-have LLMOps experience
Nice-to-have modern data platform experience such as Snowflake
Nice-to-have containers, Kubernetes and infrastructure as code experience
Nice-to-have experience in healthcare, life sciences, MedTech, financial services or other regulated industries
Nice-to-have cloud, AI or architecture certifications

Benefits

Comp & perks

Work on AI solutions that reach production in a regulated, high-impact industry
Collaborate with global teams and a client investing in scaling AI responsibly
Grow toward ownership of architecture decisions and reusable AI standards
Join a company that puts AI at the center of how it delivers technology