Generative AI Architect- Manager

PwC; Bengaluru, Gurugram District; Híbrido; INR 4.000.000 - 7.000.000; Jornada completa

Descripción de la vacante

PwC in Bengaluru is hiring a Gen AI Architect - Lead Data Scientist to design enterprise GenAI solutions, including multi-agent orchestration, LLM integration, and RAG pipelines. You will define standards, drive MVP scope, and ensure observability and governance across deployments.

With 8+ years of experience and advanced degrees, you will lead engineering teams, mentor talent, and shape AI technology choices across cloud platforms and tools.

Formación

• Bachelor's and master's degrees required.

Responsabilidades

• Architect end-to-end GenAI solutions focusing on agentic system designs, including multi-agent orchestration and tool-use chains.
• Define observability, testing, and production SLAs for agentic AI solutions.
• Lead architecture reviews and mentor GenAI/Agentic AI teams.
• Collaborate with stakeholders to translate objectives into agentic AI requirements and MVP scope.
• Evaluate and implement AI stacks and orchestration tools, ensuring security and governance compliance.

Conocimientos

Generative AI

Architectural leadership

Observability

DevOps

LLMOps

Python

Cloud platforms

Educación

Bachelor's degree

Master's degree

Herramientas

Pinecone

Weaviate

Azure AI Search

LangSmith

Langfuse

Arize AI

LangChain

Kubernetes

Docker

Azure DevOps

GitHub Actions

Descripción del empleo

Gen AI Architect - Lead Data Scientist

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 risksprompt injection, jailbreaking, data exfiltration, tool misuseand 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.