Ranking: 42
As a Solution Architect – Generative AI, you will be responsible for the architecture, design, and technical leadership of enterprise-grade generative AI solutions. You will define architectural blueprints, validate them through prototypes and PoCs, and guide engineering and data science teams through delivery—ensuring solutions are scalable, secure, ethical, and production-ready.
Design and implement end-to-end generative AI solution architectures aligned with business needs and industry best practices.
Translate complex business challenges into scalable, secure, and cost-efficient GenAI architectures.
Evaluate and select architectural patterns, frameworks, and orchestration approaches to ensure performance and maintainability.
Design APIs, data pipelines, integrations, and deployment strategies supporting GenAI workloads.
Develop prototypes and proof-of-concepts to validate architectural decisions and demonstrate business value.
Stay current with GenAI design patterns, agentic frameworks, orchestration tools, and MCP (Model Context Protocol).
Assess and integrate LLMs, vector databases, orchestration layers, and agentic approaches into enterprise ecosystems.
Define extensible architectures that support multi-model, multi-provider strategies.
Embed ethical AI principles, governance frameworks, and regulatory compliance into solution architectures.
Ensure GenAI solutions align with responsible AI practices, including transparency, explainability, and risk mitigation.
Partner with security, legal, and governance teams to ensure compliance throughout the solution lifecycle.
Provide technical leadership and mentorship to engineering and data science teams.
Communicate complex architectural concepts clearly to technical and non-technical stakeholders.
Challenge assumptions and influence architectural direction across diverse audiences.
Strong expertise in Generative AI architecture, including enterprise design patterns and solution blueprints.
Deep experience with cloud platforms, especially AWS, including services such as:
SageMaker
Amazon Bedrock
Vector databases
Familiarity with MCP (Model Context Protocol) and its role in standardized, extensible agentic systems.
Proven ability to assess, select, and integrate GenAI models, orchestration frameworks, and agentic approaches.
Solid background in solution architecture, including system integration, APIs, data pipelines, and deployment strategies.
Excellent stakeholder management and communication skills.
Strong awareness of ethical considerations, governance, and responsible AI design.
5+ years in AI/ML solution architecture, with at least 1 year focused on Generative AI.
Proven delivery of enterprise-grade AI systems on AWS or other major cloud platforms.
Relevant certifications such as:
AWS Certified Solutions Architect
AWS Certified Machine Learning – Specialty
Google Professional Machine Learning Engineer
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related fields.
5+ years of overall experience in architecture roles.
English level: C1 (professional fluency required).
As a Solution Architect – Generative AI, you will be responsible for the architecture, design, and technical leadership of enterprise-grade generative AI solutions. You will define architectural blueprints, validate them through prototypes and PoCs, and guide engineering and data science teams through delivery—ensuring solutions are scalable, secure, ethical, and production-ready.
Design and implement end-to-end generative AI solution architectures aligned with business needs and industry best practices.
Translate complex business challenges into scalable, secure, and cost-efficient GenAI architectures.
Evaluate and select architectural patterns, frameworks, and orchestration approaches to ensure performance and maintainability.
Design APIs, data pipelines, integrations, and deployment strategies supporting GenAI workloads.
Develop prototypes and proof-of-concepts to validate architectural decisions and demonstrate business value.
Stay current with GenAI design patterns, agentic frameworks, orchestration tools, and MCP (Model Context Protocol).
Assess and integrate LLMs, vector databases, orchestration layers, and agentic approaches into enterprise ecosystems.
Define extensible architectures that support multi-model, multi-provider strategies.
Embed ethical AI principles, governance frameworks, and regulatory compliance into solution architectures.
Ensure GenAI solutions align with responsible AI practices, including transparency, explainability, and risk mitigation.
Partner with security, legal, and governance teams to ensure compliance throughout the solution lifecycle.
Provide technical leadership and mentorship to engineering and data science teams.
Communicate complex architectural concepts clearly to technical and non-technical stakeholders.
Challenge assumptions and influence architectural direction across diverse audiences.
Strong expertise in Generative AI architecture, including enterprise design patterns and solution blueprints.
Deep experience with cloud platforms, especially AWS, including services such as:
SageMaker
Amazon Bedrock
Vector databases
Familiarity with MCP (Model Context Protocol) and its role in standardized, extensible agentic systems.
Proven ability to assess, select, and integrate GenAI models, orchestration frameworks, and agentic approaches.
Solid background in solution architecture, including system integration, APIs, data pipelines, and deployment strategies.
Excellent stakeholder management and communication skills.
Strong awareness of ethical considerations, governance, and responsible AI design.
5+ years in AI/ML solution architecture, with at least 1 year focused on Generative AI.
Proven delivery of enterprise-grade AI systems on AWS or other major cloud platforms.
Relevant certifications such as:
AWS Certified Solutions Architect
AWS Certified Machine Learning – Specialty
Google Professional Machine Learning Engineer
Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or related fields.
5+ years of overall experience in architecture roles.
English level: C1 (professional fluency required).
