Lead Consultant - AI Architect

Job description

Job Title: Lead Consultant - AI Architect Career Level: E Introduction to role: Are you ready to architect agentic AI at enterprise scale and turn enterprise data into decisions that accelerate life-changing medicines to patients? Do you want to help us set the standards and platforms that make AI secure, interoperable, and fast to deliver across a global organization? In this role, you will define the enterprise architecture for agentic AI, foundation models, and the Model Context Protocol, helping us scale platforms and enable innovative AI products across teams. You will connect strategy to execution by crafting tailored solutions using Amazon Web Services, OpenAI, Databricks, Amazon Bedrock, Amazon Q, SageMaker, Landing AI, and emerging technologies—ensuring alignment with data strategy and pharmaceutical regulations while unlocking measurable business value. You will collaborate with research, governance, engineering, and product teams as we deliver AI capabilities that are responsible by design. From first use case to production scale, you will establish repeatable patterns. You will reduce friction and build the technical backbone. This backbone powers self-directed processes, inquisitive data products, and knowledge systems. Accountabilities: • Define enterprise AI architecture strategy aligned to our business and R&D priorities; set roadmaps that scale across domains and deliver measurable outcomes. • Lead architecture for agentic AI, multi-agent orchestration, and foundation models (LLMs, multimodal), enabling autonomous workflows and intelligent operations. • Establish reference architectures and standards across open-source and commercial AI platforms to drive secure, compliant, and repeatable delivery. • Advance adoption of MCP to improve interoperability and context management across agents, applications, and data products. • Scalable AI Platforms: Design scalable platforms across AWS, OpenAI, Databricks, and hybrid environments; ensure elasticity, reliability, and cost control. • Support the full lifecycle from use case and MVP through deployment, optimization, monitoring, and continuous improvement. • Architect RAG, vector databases, knowledge systems, and graph services to unlock enterprise knowledge and accelerate decision-making. • Responsible AI, Governance, and Risk: Translate emerging regulations into clear standards and controls; partner with security and risk leaders to mitigate threats such as data poisoning, model theft, and adversarial attacks. • Work across business, data, and engineering teams to align initiatives; turn partner insights into scalable solutions linked to our business outcomes. • Partner with data scientists and AI specialists to identify and pilot important use cases. Assess feasibility with interested parties. Challenge initiatives that do not align or are unfeasible. • Delivery Alignment: Gather input from business users, data scientists, security teams, data engineers, analysts, and operations; translate strategy into practical solutions that scale. • Technology Selection and Deployment Models: Define AI architectures and select fit-for-purpose technologies spanning both open-source projects and commercial solutions; recommend cloud, on-premises, or hybrid models; ensure integration with data, analytics, and enterprise platforms. • Continuous Improvement and MLOps: Evaluate tools and practices across data, models, and software engineering; establish feedback loops; measure performance; support recalibration and retraining. • Architecture and Pipeline Planning: Apply deep understanding of ML and deep learning workflows and trade-offs across data management, governance, model development, deployment, and production operations. • Engineering Excellence: Champion modern software engineering and DevOps practices including Git, containers, Kubernetes, and CI/CD to accelerate delivery while improving reliability. • Deliver conceptual and logical models for autonomous workflows, intelligent data products, and operations led by automated agents. Develop agentic data pipelines, real-time information flows, knowledge graphs, and metadata for adaptive, self-governing capabilities. • Business Partnership and Strategy Evolution: Partner with business leaders to evolve AI designs in line with strategy; own AI designs for large or complex capability domains. • Transformation Program Leadership: Take accountability for enterprise architecture in major transformation programs; produce and review blueprints to keep change initiatives aligned. • Data Governance Integration: Collaborate with Data Offices to embed governance into builds; provide assurance via standard metrics such as master data usage and data classification metadata. • Patterns for Analytics and Digital: Select or define architectures and patterns supporting reporting, analytics, data science, digital, and operational use cases. • Strategic and Tactical Architecture Support: Provide planning, design expertise, and delivery support including technical designs, technology standards, models, and enterprise considerations. • Integration Architecture: Support or lead development of AI integration architecture and end-to-end data integration designs for projects. • Standards and Approvals: Secure approval for AI artifacts; implement standard enterprise data element names, abbreviations, characteristics, and domains throughout the project lifecycle. • Work Package and Demand Management: Define and handle work packages for teams and flexible resources; plan demand and recharge for AI and techn