Primary Skills

Define and implement AI governance frameworks covering security, compliance, and ethics 

Develop and track enterprise AI/GenAI roadmap, adoption strategy, and maturity models 

Identify high-impact use cases (automation, copilots, agents, knowledge systems)

Design end-to-end GenAI architectures across Azure or GCP ecosystems, including RAG pipelines, vector databases, embeddings, and agentic AI frameworks

Architect scalable AI solutions leveraging API integrations, orchestration layers, and cloud-native services (Azure, GCP)

Extensive experience across multi-cloud platforms including Azure AI services (Azure OpenAI, Cognitive Services, ML Studio) or GCP (Vertex AI, AI APIs, BigQuery ML)

Strong expertise in AI/ML and GenAI tools such as OpenAI, LangChain, Semantic Kernel, Vertex AI Agents, Hugging Face, vector databases (Pinecone, FAISS, Azure AI Search), and MLOps frameworks

Translate business problems into AI solutions with clear TCO/ROI and KPI frameworks

Communicate complex AI concepts to leadership and clients in simple business terms

Drive AI adoption programs with training, change management, and stakeholder alignment

Lead end-to-end execution (PoCs → pilots → enterprise rollout) and scale Agentic AI solutions

Work with business leaders and engineering teams to execute AI solutions at scale

Collaborate within the Capgemini ecosystem to leverage best assets and accelerators

Define and manage reference architectures, design standards, and AI governance

Develop executive dashboards (KPIs, ROI tracking) and ensure strong delivery tracking and oversight