The Senior Solution AI Architect will lead the end-to-end design and implementation of Generative AI and LLM-based solutions across enterprise applications. They are responsible for defining architecture standards, ensuring AI security and governance, and collaborating with stakeholders to translate business needs into scalable technical solutions.
The ideal candidate will have deep expertise in Generative AI, Large Language Models (LLMs), AI agents, RAG, cloud AI platforms, and enterprise solution architecture. This position requires a hands-on technology leader who can translate business requirements into scalable, secure, and production-ready AI solutions. Key Responsibilities
Architect and lead end-to-end AI and Generative AI solutions across enterprise applications.
Design solutions leveraging LLMs, RAG, AI agents, embeddings, vector databases, and prompt engineering.
Define enterprise AI architecture standards, patterns, and technology roadmaps.
Evaluate AI use cases and recommend appropriate models, platforms, frameworks, and architecture approaches.
Integrate AI solutions with enterprise applications, APIs, databases, and cloud platforms.
Lead architecture reviews, technical design sessions, and AI proof-of-concepts.
Work closely with business stakeholders, engineering teams, and senior leadership to translate business needs into technical solutions.
Establish best practices for AI security, governance, responsible AI, data privacy, scalability, and performance.
Provide technical leadership and mentorship to engineering and AI development teams.
Drive AI solutions from initial concept and POC through production implementation.
Required Qualifications
Extensive experience in Solution Architecture, Enterprise Architecture, or AI Architecture.
Strong hands-on experience designing Generative AI and LLM-based solutions.
Deep understanding of RAG, AI agents, embeddings, vector databases, prompt engineering, and LLM application architecture.
Experience with cloud AI platforms such as Azure AI / Azure OpenAI, AWS, or Google Cloud AI.
Strong experience with Python, APIs, microservices, databases, and cloud-native architecture.
Experience architecting secure and scalable enterprise applications.
Knowledge of MLOps/LLMOps, model evaluation, monitoring, and deployment.
Strong understanding of AI governance, responsible AI, security, and data privacy.
Excellent communication, technical leadership, and stakeholder-management skills.
Ability to communicate complex AI concepts to both technical and non-technical stakeholders.
Preferred Qualifications
Experience building enterprise AI copilots, intelligent assistants, and agentic AI solutions.
Experience taking GenAI solutions from POC to enterprise production environments.
Knowledge of enterprise search and vector database technologies.
Experience driving AI technology strategy and architecture decisions.
Relevant Cloud, AI, or Solution Architecture certifications are preferred.
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