Enterprise AI Architect

Role Purpose The AI Technical Director will provide senior technical leadership for complex enterprise AI, generative AI and agentic AI engagements across APAC. The role will shape solution strategy, lead architecture and engineering decisions, guide delivery from discovery through production, and build trusted relationships with senior client and partner stakeholders. The successful candidate will combine deep technical credibility with consulting judgement, commercial awareness and the ability to mobilize multidisciplinary teams at scale. Key Responsibilities AI strategy and advisory: Translate business priorities into pragmatic AI roadmaps, target architectures, investment cases and delivery plans. Advise clients on where AI can create value and where conventional analytics, automation or software engineering maybe more appropriate. Solution architecture: Own end-to-end architecture for AI and GenAI solutions, including data ingestion, model selection, retrieval-augmented generation, agent orchestration, integration, security, observability, evaluation and human oversight. Technical leadership: Lead technical discovery, design reviews, proofs of concept and production delivery. Set engineering standards and resolve complex architecture, performance, scalability and integration challenges. Responsible and secure AI: Embed privacy, security, safety, explainability, model risk management and regulatory considerations into solution design and delivery governance. Delivery assurance: Provide technical governance across multiple workstreams, manage technical risks and dependencies, and ensure solutions meet quality, resilience, performance and operational-readiness expectations. Client leadership: Engage credibly with CIOs, CTOs, CDOs, business executives, enterprise architects and risk leaders. Communicate complex technical choices in clear business language. Growth and solutioning: Support pursuits, workshops, proposals and estimations. Shape differentiated propositions, reusable assets and industry solutions in partnership with sales, consulting, delivery and alliance teams. People and capability building: Coach architects, data scientists, ML engineers and platform engineers. Build communities of practice, reusable patterns, accelerators and role-based development plans. Ecosystem leadership: Work across hyperscalers, model providers, data platforms and specialist technology partners to select fit-for-purpose components and drive joint innovation. Required Experience and Qualifications • Typically 15+ years of experience in technology consulting, solution architecture, software engineering, data and AI, including significant leadership of enterprise-scale AI programs. • Proven experience designing and delivering production AI, machine learning or generative AI solutions in complex enterprise environments. • Deep understanding of modern GenAI patterns, including foundation models, prompt engineering, RAG, vector search, tool use, agentic workflows, evaluation, guardrails and LLMOps. • Strong grounding in data architecture, cloud-native engineering, APIs, event-driven integration, security, DevSecOps, MLOps and platform operations. • Hands-on familiarity with leading cloud and AI ecosystems such as Microsoft Azure, AWS, Google Cloud, Databricks, Snowflake, OpenAI or comparable platforms. • Experience leading multidisciplinary and geographically distributed teams across architecture, engineering, data science, product, design, security and change. • Demonstrated consulting capability, including executive workshops, problem structuring, proposal development, estimation and delivery governance. • Excellent written and verbal communication, with the ability to influence technical and non-technical stakeholders. • Bachelor's or Master's degree in computer science, engineering, data science, mathematics or a related discipline. Relevant architecture, cloud, data or AI certifications are advantageous. Equal Opportunity Statement Capgemini is committed to fair and merit-based employment practices. All qualified applicants will be considered based on skills, experience, and job-related competencies, in line with the principles of fair employment.