Lead AI Solutions Architect

As Lead AI Solution Architect, you drive production-grade AI across business domains from the Enterprise Data Office. You will translate strategy into scalable, secure AI products, guiding design, governance, and delivery while mentoring teams. You’ll own end-to-end AI systems, leveraging AWS, Databricks, and modern data architectures to deliver measurable business value. This role combines hands-on technical leadership with enterprise collaboration to shape domain AI strategy and platforms.

• Act as the primary AI solutions architect and trusted advisor from ideation to production
• Ensure AI architecture principles deliver measurable business value
• Collaborate with stakeholders to understand use cases and constraints
• Design end-to-end AI systems aligned with enterprise standards (domain capabilities, AI lifecycle management, data patterns)
• Review designs for security, scalability, resilience, and cost-effectiveness
• Shape domain data and AI strategy and roadmaps with domain leadership
• Represent the function in central architecture forums and provide feedback to evolve blueprints
• Balance governance with enablement and accelerate delivery
• Identify opportunities where data/ML/AI drive business value
• Advise on MLOps, LLMOps, AgentOps prioritization and sequencing
• Support maturation of domain data product and AI operating model
• Apply deep knowledge of modern AI architectures on AWS and responsible AI practices

• Deep knowledge of AWS-based AI architectures, including Generative AI, RAG, and large-scale asynchronous inference
• Expertise designing and operating AWS/Databricks with model serving, vector search, and foundation model integration
• Strong understanding of AI security (private model endpoints, PII masking in prompts, IAM least-privilege, secure data egress/ingress)
• Infrastructure-as-Code (Terraform) and containerization (Docker, Kubernetes, Helm) for scalable AI platforms
• Hands-on MLOps/LLMOps with automated evaluation, drift detection, CI/CD for models, and real-time inference resilience
• Experience with AI FinOps and cost/performance trade-offs between proprietary and open-source models
• Proficiency in AI-native development workflows, Agentic IDEs, and rapid prototyping
• Experience designing multi-agent orchestration workflows (LangGraph, CrewAI) and bridging LLM reasoning with enterprise data actions
• Strong stakeholder communication and leadership capabilities

• Stakeholder communication
• Leadership and collaboration
• Strategic thinking
• AWS
• Databricks
• MLOps