Lead AI Solutions Architect

We are seeking a Lead AI Solution Architect to serve as the primary technical catalyst for AI within our business domains. Embedded directly within the business from the Enterprise Data Office Solution Architecture team, you will be the domain’s trusted authority for identifying, designing, and accelerating the delivery of production-grade ML, GenAI, and Agentic AI solutions. Your mission is to bridge the gap between high-level AI strategy and tangible business impact. You will lead the ideation and engineering of high-value AI products ensuring they are scalable, secure, and resilient while utilizing real-world delivery experience to refine our internal AI platforms and engineering standards. This is a hands-on leadership role requiring deep expertise in AWS and Databricks, modern cloud data architectures, and the ability to influence senior stakeholders through clear technical communication. Key Accountabilities Architectural Enablement & Leadership Act as the primary AI solutions architect and trusted advisor for the business function, supporting initiatives from ideation through to production. Ensure enterprise Data, ML, and AI architecture principles are applied pragmatically to deliver measurable business value. Solution Architecture & Design Partner with business stakeholders, product managers, and engineering teams to understand business use cases and constraints. Design and document end-to-end AI systems architectures, aligned with enterprise standards for: Domain-aligned AI capabilities and reusable AI services AI lifecycle management frameworks (MLOps, LLMOps, AgentOps) AI-aligned data patterns (feature stores, RAG pipelines, inference architectures) Review and guide solution designs to ensure they are secure, scalable, resilient, and cost-effective. Collaboration & Enterprise Alignment Work closely with domain leadership to help shape domain data and AI strategy and roadmaps. Act as the voice of the business domain within the central Enterprise Architecture and AI communities. Provide structured feedback to central teams to evolve enterprise blueprints, platforms, and governance models based on practical experience. Governance, Risk & Best Practices Represent the business function in Central Design Authority and architectural review forums. Ensure solutions meet enterprise security, AI governance, and AI risk standards while remaining delivery-oriented and scalable. Champion architectural best practices, data literacy, and AI-responsible design within the business function. Balance governance with enablement, ensuring standards accelerate rather than hinder delivery. Strategic Guidance & Value Identification Proactively identify opportunities where data, ML, and AI initiatives can deliver significant business value. Advise on MLOps, LLMOps, AgentOps principles prioritisation and sequencing of initiatives based on feasibility, impact, and architectural readiness. Support the maturation of the domain’s data product and AI operating model.

Knowledge, Experience & Capabilities Deep knowledge of modern AI architectures on AWS, including Generative AI, RAG (Retrieval-Augmented Generation), and large-scale asynchronous inference workloads. Ability to translate enterprise business problems into AI-driven Products, promoting decentralized model ownership while maintaining central standards for safety, evaluation, and reliability. Expert-level experience designing and operating AWS/DataBricks with strong mastery of Model Serving, Vector Search and the integration of Foundation Models. Strong understanding of AI security architectures, including private model endpoints, PII masking/redaction in prompts, IAM least-privilege for model access, and secure data egress/ingress for external LLM providers. Strong understanding of capability in Infrastructure-as-Code (Terraform) and Containerization (Docker, Kubernetes, Helm) to architect scalable, reproducible, and environment-agnostic AI platforms; specifically optimized for GPU-accelerated compute, distributed model training, and high-availability inference services. Hands-on experience applying MLOps and LLMOps principles, including automated model evaluation, drift detection, CI/CD for model weights/code, and the operational resilience of real-time inference APIs. Demonstrated ability to apply AI FinOps (Token Economics), managing the cost-performance trade-offs between proprietary LLMs and fine-tuned open-source models (e.g., Llama/Mistral). Proficiency in “vibe coding” and AI-native development workflows, utilizing Agentic IDEs and LLM-driven code generation to rapidly prototype, iterate, and deploy production-grade AI components at high velocity. Proven experience designing multi-agent orchestration workflows (e.g., LangGraph, CrewAI) and utilizing MCPs to bridge the gap between LLM reasoning and enterprise data actions.