PwC
Generative AI Architect- Manager
India · Work style not stated · Full time
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About the role
Description from the employer’s posting. Check the employer’s page for the current version.
- EmployerPwC
- LocationIndia
- Work styleWork style not stated
- Employment typeFull time
- Employer posted
Industry/Sector
Not Applicable
Specialism
Data, Analytics & AI
Management Level
Manager
Job Description & Summary
The Opportunity
Join our Acceleration Center India and help shape the future of business for our diverse client portfolio across geographies and jurisdictions. You’ll work at the heart of global teams across Advisory, Assurance, Tax and Business Services—solving real client challenges through connected collaboration. We’ll help you grow your skills so you can go further. With hands-on learning, cutting-edge tools and an inclusive culture, this is your opportunity to do inspiring work that makes a difference—every day.
As a Generative AI Architect you will help shape client solutions within our Data and Analytics Engineering practice, drawing on data science methods to design and develop generative AI capabilities that turn data into actionable insights. As a Manager you will lead teams, coach and develop people, manage workstreams, and make decisions that support delivery across complex client needs while using technology and innovation to strengthen outcomes. In this role at PwC, you will guide solution design, support stakeholder discussions, and bring together analytics, engineering, and AI concepts to help clients address business challenges and move ideas into delivery.
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Responsibilities
- Architect end-to-end enterprise GenAI solutions focusing on agentic system designs, including multi-agent orchestration, autonomous task execution, tool-use chains, LLM integration, and RAG pipelines.
- Define and enforce agentic design standards across teams covering agent communication protocols, intelligent task routing, lifecycle management, shared tool registries, and orchestration framework selection (e.g., LangGraph, CrewAI, AutoGen, Semantic Kernel Agents, cloud-based frameworks).
- Align technology choices with clients’ preferred cloud platforms and infrastructure, ensuring compliance, cost efficiency, portability, and seamless integration with existing governance policies and services.
- Collaborate with business stakeholders to translate objectives into agentic AI requirements, defining agent capabilities, success metrics, and MVP scope for iterative delivery.
- Establish robust observability, testing, and operational standards for agentic AI solutions including telemetry, monitoring, automated regression tests, simulation environments, and production SLAs to ensure reliability.
- Lead and mentor GenAI/Agentic AI engineering teams through architecture reviews, code-level guidance, sprint planning, and quality assurance to ensure high-quality solution delivery.
- Evaluate, select, and implement agentic AI technology stacks encompassing vector databases (Pinecone, Weaviate, Azure AI Search), orchestration frameworks, observability tools (LangSmith, Langfuse, Arize AI), and AI-powered development tools, complemented by strong expertise in DevOps, LLMOps, containerized architectures, and CI/CD pipelines (Azure DevOps, GitHub Actions).
What You Must Have
- At least a Bachelor's & Master's Degree
- At least 8 years of experience
- Oral and written proficiency in English required
What Sets You Apart
- Over4years of experience in developing and scaling Generative AI projects from prototypes to enterprise production, managing throughput, latency, cost, and multi-region deployments.
- Proven expertise implementing AI interoperability protocols like MCP (Model Context Protocol) and A2A (Agent-to-Agent) at scale for seamless system integration.
- Skilled in using enterprise cloud AI platforms such as Azure AI Foundry, Amazon Bedrock, and Google Vertex AI to build and deploy production-grade agentic AI solutions.
- Advanced Python programming skills and hands-on experience with agentic AI frameworks including LangChain, LangGraph, CrewAI, and AutoGen for building robust generative AI applications.
-Deep understanding of advanced Retrieval Augmented Generation (RAG) architectures (Graph RAG, Vectorless RAG, Hybrid RAG) and traditional AI/ML fundamentals like model building, fine-tuning, and evaluation.
- Strong knowledge of LLM security risks—prompt injection, jailbreaking, data exfiltration, tool misuse—and experience designing defense-in-depth safeguards within agentic system architectures.
- Expertise in containerization and cloud-native orchestration (Kubernetes, Docker, serverless) and event-driven architectures for scalable deployment of agentic AI workloads; holds relevant AI or solution architecture certifications.
Travel Requirements
Not Specified
Job Posting End Date
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At a glance
- Employment typeFull time
- Employer posted
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