Global S&C GN - AI -AI Labs- Manager

Manager – Applied Intelligence

GenAI Solutions Architect (Client Solutioning & End-to-End GenAI Apps)

Accenture Applied Intelligence practice help our clients grow their business in entirely new ways. Analytics enables our clients to achieve high performance through insights from data - insights that inform better decisions and strengthen customer relationships. From strategy to execution, Accenture works with organizations to develop analytic capabilities - from accessing and reporting on data to predictive modelling - to outperform the competition.

As part of our Analytics practice, you will join a worldwide network of smart and driven colleagues experienced in leading tools, methods and applications. From data to analytics and insights to actions, our forward-thinking consultants provide analytically-informed, issue-based insights at scale to help our clients improve outcomes and achieve high performance.

Manager in Applied Intelligence

A GenAI Solutions Architect leads client-facing GenAI solutioning and delivery—translating business problems into secure, scalable GenAI application architectures and roadmaps. They design end-to-end GenAI products (copilots, chatbots, decision assistants, agentic workflows) integrated with enterprise data and systems, and ensure quality through evaluation, guardrails, and responsible AI practices.

Duties And Responsibilities

• Lead client workshops to understand business objectives, user journeys, and decision workflows; define use case scope, success metrics, and phased roadmap (MVP → scale).
• Own end-to-end solution architecture for GenAI apps:
• Conversation orchestration and tool/function calling patterns
• RAG designs (grounding, retrieval tuning, chunking, reranking, citations)
• Integration with enterprise systems via APIs and workflows
• Non-functional requirements: security, privacy, performance, resiliency, cost
• Design agentic workflows for enterprise use cases: action execution, exception handling, human-in-the-loop approvals, and audit trails.
• Define prompt engineering approach and response controls (templates, few-shot patterns, fallback strategies) and guide implementation.
• Establish evaluation strategy: offline/online testing, golden datasets, regression testing for prompts/retrieval, and continuous improvement loops.
• Drive engineering delivery: backlog, sprint planning, technical governance, integration testing, and production readiness.
• Collaborate with data engineering, BI/analytics, and platform teams to align semantic/knowledge sources and ensure trusted grounding.
• Create client-ready deliverables: architecture diagrams, implementation plan, operating model recommendations, and enablement/training plan.
• Support pursuits and proposals: solution narratives, estimations, staffing plans, and risk/dependency management.
  

Educational Background/qualifications

• B Tech/M Tech from reputed engineering colleges
• Masters/M Tech in Computer Science
  
  

Master degree in Statistics/Econometrics/ Economics from reputed institute