Generative AI Architect (Data Science lead)

PwC; Bengaluru, Gurugram District; Hybride; INR 4 000 000 - 7 000 000; Plein temps

Résumé du poste

PwC is seeking a Gen AI Data Science Lead (Architect) to architect end-to-end enterprise GenAI solutions, focusing on agentic system designs, multi-agent orchestration, tool-use chains, and RAG pipelines. You will define standards, select frameworks, and drive enterprise-level delivery from design to production in a cloud-first environment.

You will collaborate with stakeholders to translate objectives into AI requirements, lead GenAI teams, ensure observability and reliability, and align

Qualifications

• At least a Bachelor's & Master's Degree.
• At least 8+ years of experience in AI/DS leadership.
• Proficiency in English (oral and written).

Responsabilités

• Define and enforce agentic design standards across teams including communication, routing, lifecycle mgmt, tool registries, and orchestration framework selection.
• Align tech choices with client cloud platforms, ensuring cost efficiency, portability, and governance compliance.
• Translate objectives into agentic AI requirements with MVP scope for iterative delivery.
• Establish observability, testing, and operation standards with telemetry, regression tests, simulations, and SLAs.
• Lead and mentor GenAI/Agentic teams with architecture reviews, code guidance, and QA.
• Evaluate and implement agentic AI stacks including vector DBs, orchestration, observability tools, and DevOps pipelines.

Connaissances

GenAI

Architecture

Cloud platforms

Python

MLOps

Kubernetes

LangChain

RAG

Formation

Bachelor's degree

Master's degree

Outils

Pinecone

Weaviate

Azure AI Search

LangSmith

Langfuse

Arize AI

Azure DevOps

GitHub Actions

Description du poste

Gen AI Data Science lead (Architect)

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.