Lead Data Scientist

Capgemini•San Francisco, CA

About The Position

Own AI architecture for Capgemini’s largest and most strategic client programs. The role is accountable for architecture across complex, multi-workstream AI, generative AI, and agentic AI programs, from initial pursuit conversations through production. It advises senior technology and business executives, governs architecture decisions, and ensures proposed solutions are secure, scalable, cost-effective, deliverable, and tied to measurable business outcomes. The position leads end-to-end AI architecture spanning foundation models, RAG and knowledge systems, agents and orchestration, data and integration platforms, cloud and hybrid infrastructure, and MLOps/LLMOps. It also shapes strategic pursuits, advises on platforms and vendors across Azure, AWS, GCP, and the wider AI ecosystem, and sets architectural standards and guardrails for security, privacy, Responsible AI, and regulatory compliance. The role leads architecture through delivery, from detailed design and engineering mobilization to implementation assurance, production readiness, operational transition, and remediation of architecture deviations. It takes responsibility for business outcomes and FinOps discipline, and develops Capgemini’s AI architecture capability through mentoring, reusable reference architectures, alliances, and thought leadership.

Requirements

10+ years in solution, enterprise, platform, or cloud architecture.
3+ years designing AI/ML or GenAI solutions, including production deployments.
Proven ownership of complex enterprise architecture.
Client-facing consulting and executive stakeholder experience.
Production experience with at least one hyperscaler AI ecosystem.
Evidence of solution shaping and delivery governance.
Strong grounding in security, data, integration, and Responsible AI.

Nice To Haves

Experience across more than one hyperscaler.
Experience in regulated industries.
TOGAF or hyperscaler architecture certifications.
Experience creating reusable assets or reference architectures.
Public thought leadership, patents, publications, or conference participation.
Knowledge of specific AI frameworks and data platforms.

Responsibilities

Own end-to-end AI architecture across multiple workstreams or programs, including foundation models, RAG and knowledge systems, agents and orchestration, data and integration platforms, cloud and hybrid infrastructure, and MLOps/LLMOps.
Lead AI solution shaping for strategic pursuits, defining architecture, delivery approach, effort, risks, differentiators, and commercial assumptions.
Advise CIOs, CTOs, CDOs, and business executives on AI architecture strategy, platform and vendor choices, and roadmaps from early use cases to enterprise scale.
Govern architecture decisions as design authority; establish standards, reference architectures, and guardrails for security, privacy, Responsible AI, and regulatory compliance.
Lead architecture through delivery, including detailed design, engineering mobilization, implementation assurance, production readiness, operational transition, and remediation of architecture deviations.
Link architecture decisions to measurable client value and apply FinOps discipline to forecast, monitor, and optimize model, compute, and platform costs.
Mentor and grow architects, create reference architectures, and build market credibility through alliances and thought leadership.