AI Solution Architect I

Job Summary

We are seeking a seasoned Generative AI Architect with a strong foundation in enterprise and system architecture, combined with significant depth and breadth in Generative AI (GenAI) and Agentic AI application development. This is a senior cross-cutting role responsible for architecting, designing, and guiding the implementation of Industry AI Solutions (IAIS) across multiple teams.

The Generative AI Architect will serve as the technical authority for AI architecture decisions within their assigned teams, working alongside embedded GenAI Developers (who report to this architect for technical direction), traditional software engineers, and business stakeholders. The architect bridges the worlds of robust enterprise system design and cutting-edge AI capabilities - translating business goals into scalable, secure, production-ready GenAI and Agentic AI solutions.

Key Responsibilities

Cross-team Architecture & Technical Leadership Architect and own the end-to-end Solution Design, working alongside a System Architect or alone for an Industry AI Solution Team, ensuring architectural consistency, pattern reuse, and alignment with enterprise standards. Serve as the primary technical authority for GenAI and Agentic AI architecture decisions within assigned teams, providing direction to embedded GenAI Developers and guiding their implementation work. Define and enforce architecture standards, design patterns, reference architectures, and guardrails for GenAI and Agentic AI development across teams. Lead architecture reviews, proof-of-concept evaluations, and technical design discussions for AI features and components. Generative AI & Agentic AI Solution Design Design scalable, production-grade GenAI application architectures including Retrieval-Augmented Generation (RAG) pipelines, Agentic Workflows, LLM integration layers, prompt management systems, and response evaluation frameworks. Architect multi-agent systems and Agentic AI workflows: agent orchestration patterns, tool use, memory management, long-horizon task execution, and human-in-the-loop designs. Select and evaluate appropriate LLMs, embedding models, and AI frameworks (commercial and open-source) based on performance, cost, latency, and compliance requirements. Define LLM fine-tuning and adaptation strategies (RLHF, PEFT, LoRA, prompt tuning) where required, overseeing implementation by the developer team. Design LLMOps and AI observability pipelines - including evaluation frameworks, tracing, monitoring, and feedback loops for deployed AI systems. Enterprise System Architecture Apply deep system architecture expertise to ensure GenAI solutions integrate seamlessly into existing enterprise platforms including CRM, ERP, workflow automation tools, and analytics systems. Design robust, secure API layers, microservices, and event-driven integration patterns that connect AI components with enterprise backends. Ensure AI architectures meet enterprise non-functional requirements: scalability, availability, latency, security, and disaster recovery. Collaborate with infrastructure and cloud teams to architect AI workloads on cloud platforms (Azure preferred: Azure OpenAI, Azure AI Studio, Azure ML) with appropriate cost and performance optimisation. Data Engineering & AI Data Architecture Design and govern data pipelines supporting AI model inference and (where applicable) fine-tuning: ingestion, transformation, quality validation, and versioning. Define data architecture for AI: vector databases, knowledge graphs, embedding stores, and structured/unstructured data integration patterns. Collaborate with data engineering teams to ensure data pipelines meet quality, freshness, and compliance requirements for AI use cases. Responsible AI, Governance & Security Embed AI fairness, transparency, explainability, and accountability principles into solution designs from inception. Define and enforce AI security architecture: prompt injection protection, data leakage prevention, model access controls, and audit logging. Ensure compliance with applicable AI governance frameworks, data privacy regulations (e.g., GDPR), and enterprise AI policies. Conduct AI risk assessments for new solutions and advise teams on responsible deployment practices. Stakeholder Collaboration & Communication Collaborate closely with executive stakeholders, product managers, business analysts, and engineering lead

Skill Requirements

Architecture & Engineering Foundation Bachelor's or Master's degree in Computer Science, Software Engineering, AI, Data Science, or a related field. Proven experience as a Solution Architect or Enterprise Architect (5+ years), with a track record of designing large-scale, production enterprise systems. Strong command of system design principles: distributed systems, microservices, event-driven architecture, API design, cloud-native patterns. Experience architecting across cloud platforms, with strong preference for Azure (Azure OpenAI, Azure AI Studio, Azure ML, Azure API Management). Generative AI & Agentic AI Expertise Deep hands-on experience with LLMs and GenAI application development - including RAG architecture, LLM integration, prompt engineering, and chain-of-thought design. Proven experience architecting Agentic AI systems and multi-agent workflows using frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or equivalent. Strong understanding of LLM eval

Other Requirements

• Experience leading AI architecture across multiple concurrent teams.
• Familiarity with MLOps tooling and platforms (MLflow, Azure ML Pipelines, Weights & Biases).
• Exposure to multimodal AI models (vision-language, speech-to-text, document AI).
• Experience with enterprise AI governance and compliance frameworks (EU AI Act, NIST AI RMF, or equivalent).
• Hands-on experience with AI-assisted development workflows and coding agents (e.g., GitHub Copilot, Cursor, or custom coding agents).
• Prior experience in a product architecture role delivering Industry AI Solutions at enterprise scale.