AI Solutions Architect at MSXInternational

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