AI Transformation & Delivery Manager

Capgemini is seeking a senior AI leader to help scale its Seattle-based AI & Analytics team and support a growing portfolio of strategic engagements and pursuits across Microsoft and other major local accounts. This is not a traditional individual-contributor Data Scientist role. The position combines enterprise AI architecture, hands-on technical credibility, client delivery, business development, account collaboration, and people leadership.

The successful candidate will operate as a trusted partner to clients, Capgemini account teams, Insights & Data go-to-market leaders, and AI&A leadership. The role will help shape opportunities, mobilize delivery teams, guide enterprise-grade AI solutions from idea through production, and mentor a growing team of early-career and experienced practitioners.

What Success Looks Like

• Expand Capgemini's AI presence in the Seattle market by connecting technical capability to credible client opportunities and measurable business outcomes.
• Provide day-to-day leadership capacity for the Seattle AI&A team, including staffing, coaching, skills development, quality oversight, and team engagement.
• Convert AI concepts and prototypes into secure, governed, production-ready solutions and reusable enterprise capabilities.
• Build strong working relationships across clients, account teams, go-to-market leaders, partners, and AI&A leadership while maintaining clear ownership and follow-through.
• Balance hands-on technical depth with practical consulting judgment, commercial awareness, and delivery realism.

Key Responsibilities

AI Transformation, Strategy & Architecture

• Lead discovery and executive working sessions to frame business problems, prioritize AI use cases, define value hypotheses, and create phased transformation roadmaps.
• Architect enterprise AI solutions spanning predictive AI, Generative AI, intelligent applications, Agentic AI, conversational and multimodal experiences, and AI-assisted software engineering.
• Design platform capabilities such as model and agent gateways, orchestration, tool and API integration, data and semantic layers, identity and access, evaluation, observability, FinOps, and lifecycle management.
• Establish practical governance-by-design patterns covering Responsible AI, security, privacy, human oversight, agent identity and authorization, traceability, auditability, testing, and runtime controls.
• Guide cloud and platform choices across Azure and Microsoft technologies, while remaining credible across AWS, GCP, Databricks, Snowflake, and relevant partner ecosystems.

Technical Leadership & Delivery

• Provide architecture and engineering leadership from pursuit and proof of value through production deployment, adoption, and operational support.
• Remain sufficiently hands-on to review code, prototypes, prompts, agent workflows, APIs, retrieval pipelines, data flows, evaluations, deployment patterns, and technical trade-offs.
• Lead or support Forward Deployment Engineer / Hyper Velocity Engineering-style engagements requiring close client collaboration, rapid iteration, and ownership of business outcomes.
• Define engineering standards for reliability, security, testing, CI/CD, LLMOps/MLOps, monitoring, incident response, and production readiness.
• Identify delivery risks early, shape realistic staffing and work plans, and ensure quality across mixed onshore, nearshore, and offshore teams.

Business Development & Account Engagement

• Partner with I&D go-to-market leaders, local account teams, client partners, and AI&A leadership to identify, qualify, shape, and advance AI opportunities.
• Contribute to client conversations, workshops, demos, proposals, RFP responses, estimates, solution narratives, staffing models, and executive presentations.
• Translate emerging AI capabilities into differentiated, industry-relevant offerings that can be sold and delivered responsibly.
• Create reusable assets, reference architectures, accelerators, demos, and points of view that improve pursuit speed and delivery consistency.

People & Team Leadership

• Serve as a people manager and visible technical leader for the Seattle AI&A community.
• Coach team members on technical growth, consulting skills, client readiness, communication, certifications, and career development.
• Create stretch opportunities for junior practitioners while providing appropriate review, escalation, and delivery support.
• Help manage team capacity, account alignment, recruiting, onboarding, succession, recognition, and performance enablement in partnership with practice leadership and staffing.
• Build an inclusive, collaborative culture that values care for people, accountability, continuous learning, knowledge sharing, and high-quality client outcomes.

Required Qualifications

• Typically 10+ years of experience in technology consulting, enterprise architecture, data/AI, software engineering, product management, product engineering, or related fields, including meaningful leadership responsibility.
• Demonstrated experience shaping and delivering enterprise AI solutions beyond experimentation, including GenAI and/or Agentic AI solutions integrated with business processes and systems.
• Strong architecture knowledge across applications, APIs, data, cloud, security, integration, identity, deployment, and operations.
• Hands-on working knowledge of Python and SQL, plus the ability to review modern AI application code, notebooks, API integrations, source repositories, and deployment configurations.
• Experience with LLM application patterns such as RAG, agent and tool orchestration, function calling, structured outputs, evaluation, guardrails, prompt and context engineering, and human-in-the-loop workflows.
• Experience with cloud AI services and modern data platforms. Strong Azure/Microsoft experience is preferred; credible AWS, GCP, Databricks, or Snowflake experience is also valuable.
• Experience leading client-facing consulting engagements, solution architecture, technical delivery, or transformation programs involving senior business and technology stakeholders.
• Business-development experience including opportunity shaping, proposal development, estimation, executive storytelling, workshops, demonstrations, or RFP responses.
• People-management or sustained team-lead experience, with evidence of mentoring, coaching, staffing, developing, and caring for team members.
• Excellent written and verbal communication, structured problem solving, executive presence, and ability to explain technical trade-offs to non-technical audiences.
• Ability to work regularly from the Bellevue/Seattle area and engage at local client sites when needed.

Preferred Qualifications

• Experience with Azure AI Foundry, Azure OpenAI, Microsoft Copilot Studio, Microsoft Fabric, Azure AI Search, Azure API Management, Microsoft Entra, Azure DevOps, GitHub, and Microsoft-aligned agent or application platforms.
• Experience with frameworks and protocols such as MCP, agent-to-agent integration, LangGraph, LangChain, AutoGen, or equivalent orchestration technologies.
• Knowledge of LLMOps/MLOps, model and agent evaluation, observability, red teaming, content safety, AI security, cost optimization, and production operations.
• Experience designing enterprise AI platforms, AI control planes, model/agent gateways, reusable agent services, semantic layers, knowledge platforms, or governed self-service capabilities.
• Experience in one or more locally relevant industries such as technology, retail/consumer products, telecommunications, aerospace/manufacturing, travel/hospitality, or utilities.
• Relevant cloud, architecture, data, AI, security, or Microsoft certifications. Forward-deployed engineering, HVE/FTE, or similar client-proximate delivery experience is a plus.

Leadership Profile

• Technically credible: can go deep enough to challenge an architecture, unblock engineers, and recognize production risks.
• Commercially minded: connects client needs to differentiated solutions, realistic investments, and measurable outcomes.
• Client focused: earns trust through preparation, clarity, pragmatism, and consistent follow-through.
• People centered: invests in junior talent, gives candid coaching, creates opportunities, and builds psychological safety with accountability.
• Enterprise oriented: thinks beyond a single model or use case to the platform, governance, operating model, adoption, and change required for scale.
• Hands-on and adaptable: comfortable moving between strategy, architecture, proposal work, delivery details, and team leadership as priorities shift.

Education

Bachelor's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, Information Systems, or a related discipline, or equivalent professional experience. An advanced technical or business degree is a plus.