AI Solutions Architect

Job Description As AI Solutions Architect you will be the technical expert responsible for designing scalable, secure, and high-performance blueprints that support turning AI concepts into production-grade enterprise assets. In this role you will bridge the gap between high-level business requirements and AI engineering execution, ensuring that AI-related initiatives are properly evaluated from a technical perspective, architected for value, modularity, scaling and long-term sustainability. This position focuses on technical design, feasibility evaluation, and architectural integrity, serving as the primary technical authority within the AI & Data Center of Excellence, ensuring architectural integrity and enabling structured handover to IT delivery teams for implementation. This is a senior individual contributor role with strong technical leadership and influence across teams. Your responsibilities will include Architectural Blueprinting & Design Recommend the reference architectures for AI related solutions across the enterprise. Design end-to-end pipelines for Generative AI, Machine Learning, and Agentic workflows. Ensure that AI solutions are modular, reusable, and aligned with enterprise security and compliance standards. Recommend the optimal technical stack for specific business use cases. Partner with AI & Data Governance to ensure architectures align with risk, compliance, and lifecycle requirements. Technical Feasibility & Scoping Conduct evaluation of the technical feasibility of AI related initiatives. Conduct rapid prototyping, POCs, MVPs, to validate AI-specific technical assumptions. Support defining technical requirements, model dependencies, and integration points for AI related initiatives. Collaborate with Security, Data, and Infrastructure teams to validate architectural assumptions and verify technical fit within the enterprise environment. Provide high-level effort estimations and resource requirements for AI implementations. Product Definition & Scalability Support defining what AI models need to move beyond "lab" environments into robust, scalable production systems. Support defining requirements for scaling Recommend optimized architectures for latency, cost-efficiency (token management), and reliability. Assess cost impact of model choices, inference patterns, and orchestration designs to recommend sustainable options. Establish patterns for AI safety, bias mitigation, and "Human-in-the-loop" architectural components, ensuring architectural decisions follow the AI governance model, including risk reviews, lifecycle stages, and required documentation. Technical Leadership & Mentorship Act as the "North Star" for tech people involved in the implementation of AI related solutions. Provide technical oversight and architecture reviews for AI related projects. Monitor emerging AI patterns (RAG, Fine-tuning, Multi-agent systems). Collaborate with the AI & Data Transformation Lead to support Value Streams and Support Functions on advisory support for AI uses cases, maintaining and evolving a set of reusable AI architecture patterns and component templates to support enterprise scaling. Technology Validation & Model Evaluation Design and execute the technical assessment of AI models, and emerging technologies to ensure enterprise-grade performance. Apply structured LLM validation frameworks to assess model performance, accuracy, safety, and technical suitability. Review external AI products and services from a technical perspective to gauge architectural fit and integration readiness. Stay at the forefront of AI research to identify and integrate new technical capabilities (e.g., multimodal models, advanced embedding techniques, reasoning models). Ensure AI related solutions maintain technical flexibility and avoid architectural lock-in through modular design and standardized AI interfaces. Maintain concise architectural documentation that supports decision-making, governance, and auditability. Success in this role means: • AI solutions successfully deployed from concept to production • Scalable, reusable architecture patterns adopted across the enterprise • Optimized cost, performance, and reliability of AI systems • Strong alignment between business needs, engineering delivery, and governance requirements Qualifications Education: Bachelor’s or Master’s Degree in Computer Science, Data Science, Software Engineering, or a related quantitative field is required. Solid foundational knowledge in Machine Learning and Software Architecture is essential. Experience: 5+ years of significant experience in designing end-to-end technical architectures for Machine Learning and Generative AI solutions. Proven track record in conducting technical feasibility assessments, rapid prototyping (POCs/MVPs), and bridging business requirements with engineering execution. Experience with enterprise-scale systems, security standards, and AI governance is critical. Skills: Expertise in AI design patterns (such as