Job Summary
The Senior Manager, AI Architecture is responsible for designing, developing, and governing the enterprise AI architecture that enables scalable, secure, and reliable AI solutions across the Commercial Business. Working closely with the Head, AI, IT, Data, Product teams, and external technology partners, the incumbent will define AI technology standards, architecture frameworks, and integration strategies to support the successful deployment of AI initiatives. This role provides technical leadership to ensure AI solutions are robust, interoperable, and aligned with the Company's technology landscape and business objectives.
Key Responsibilities
- Lead the design and development of the enterprise AI architecture, ensuring scalability, security, reliability, and alignment with business and technology strategies.
- Define AI architecture standards, technical frameworks, reference architectures, and best practices to support enterprise-wide AI adoption.
- Design end-to-end AI solution architectures, including Generative AI, machine learning, intelligent automation, knowledge management, and agentic AI applications.
- Develop AI platform architecture encompassing data pipelines, model serving, orchestration, APIs, vector databases, and enterprise integrations.
- Collaborate with AI Product, Commercial, Operations, IT, and Data teams to translate business requirements into scalable technical architectures.
- Define integration strategies for AI solutions with enterprise applications, ERP, WMS, TMS, CRM, customer platforms, and other business systems.
- Establish AI infrastructure requirements across cloud, hybrid, and on-premise environments to support AI workloads and enterprise scalability.
- Evaluate and recommend AI platforms, foundation models, cloud services, development frameworks, and technology stacks that best meet business requirements.
- Lead architecture reviews and technical governance to ensure AI solutions comply with enterprise architecture principles, security standards, and technology policies.
- Work closely with cybersecurity, infrastructure, and data teams to ensure AI solutions comply with data privacy, responsible AI, cybersecurity, and regulatory requirements.
- Establish AI model lifecycle architecture, including model deployment, monitoring, versioning, observability, and continuous improvement.
- Provide technical leadership and architectural guidance to AI engineers, data engineers, software developers, and solution architects throughout the AI development lifecycle.
- Collaborate with external technology vendors, cloud providers, system integrators, and AI solution partners to ensure successful implementation of enterprise AI platforms.
- Monitor emerging AI technologies, enterprise architecture trends, cloud innovations, and industry best practices to continuously enhance the Company's AI capabilities.
- Prepare architecture documentation, technical standards, solution blueprints, and governance reports for management and project teams.
- Drive architecture governance and technical decision-making across multiple AI initiatives to ensure consistency, maintainability, and long-term sustainability.
Requirements
- Bachelor's Degree in Computer Science, Artificial Intelligence, Software Engineering, Information Technology, Data Science, Engineering, or a related discipline. A Master's degree is an advantage.
- Minimum 8 years of experience in enterprise architecture, solution architecture, AI engineering, cloud architecture, or related technology disciplines, with at least 3 years in a technical leadership or managerial role.
- Proven experience designing and implementing enterprise AI architectures, machine learning platforms, Generative AI solutions, or intelligent automation technologies.
- Strong knowledge of enterprise architecture principles, distributed systems, microservices, APIs, event-driven architecture, and cloud-native application design.
- Hands-on experience with cloud AI platforms such as Microsoft Azure AI, AWS AI/ML, Google Cloud Vertex AI, or equivalent enterprise AI platforms.
- Strong understanding of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, prompt engineering, AI agents, model orchestration, and AI application frameworks.
- Experience with AI development frameworks and technologies such as LangChain, Semantic Kernel, AutoGen, Hugging Face, TensorFlow, PyTorch, or equivalent technologies.
- Knowledge of MLOps, model deployment, monitoring, CI/CD pipelines, model governance, and AI lifecycle management.
- Strong understanding of enterprise data architecture, data governance, security, identity management, privacy, and responsible AI principles.
- Experience integrating AI solutions with enterprise business systems such as ERP, CRM, WMS, TMS, data platforms, and APIs.
- Excellent analytical, technical leadership, stakeholder management, and communication skills, with the ability to explain complex technical concepts to both technical and business audiences.
- Experience within logistics, supply chain, transportation, or enterprise digital platforms is an advantage.
- Professional certifications in cloud architecture or enterprise architecture (e.g., Microsoft Azure Solutions Architect, AWS Solutions Architect, TOGAF) are advantageous.
