Generative AI Architect- Manager

PwC Acceleration Center India; Bengaluru; On-site; INR 3,500,000 - 6,000,000; Full time

Job summary

PwC Acceleration Center India seeks a Generative AI Architect-Manager to turn raw data into actionable insights across advisory, assurance, tax, and business services. Lead end-to-end GenAI designs, orchestrate multi-agent systems, and partner with clients to deliver impactful solutions.

You will mentor engineering teams, define MVP scope, ensure security and observability, and align technology choices with cloud platforms.

Qualifications

• Bachelor's & Master’s Degree in computer science, data science, or related field.
• 8+ years of experience in AI/ML, GenAI, or data analytics.
• Proficiency in English (oral and written).

Responsibilities

• Architect end-to-end GenAI solutions with multi-agent orchestration and RAG pipelines.
• Define agentic design standards and ensure compliance with governance policies.
• Lead and mentor GenAI/Agentic AI teams through architecture reviews and delivery.
• Collaborate with stakeholders to translate objectives into AI requirements.
• Establish observability, testing, and operational standards for AI solutions.

Skills

Python programming

LangChain

LangGraph

CrewAI

AutoGen

LLM APIs

Kubernetes

Docker

Cloud AI platforms

LLMOps

Education

Bachelor's & Master's in CS/Data Science

Tools

Pinecone

Weaviate

Azure AI Search

LangSmith

Langfuse

Arize AI

Kubernetes

Docker

Azure DevOps

GitHub Actions

Job description

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- Manager, you will play a pivotal role in transforming raw data into actionable insights, enabling informed decision‑making and driving business growth. Within our Data and Analytics Engineering practice, you will focus on leveraging advanced analytics and statistical techniques to extract insights from large datasets, helping solve complex business problems and inform strategic decisions. As a Manager, you will enhance your leadership style by motivating, developing, and inspiring others to deliver quality. You will be responsible for coaching, leveraging team members’ unique strengths, and managing performance to meet client expectations. You are expected to lead with integrity and authenticity, articulating our purpose and values in a meaningful way.

In this role at PwC Acceleration Center India, you will embrace technology and innovation to enhance your delivery and encourage others to do the same. You will take ownership of projects, confirming their successful planning, budgeting, execution, and completion. You will also address conflicts or issues, engaging in difficult conversations with clients, team members, and other stakeholders, while upholding professional and technical standards. This opportunity allows you to develop skills outside your comfort zone and encourage others to do the same, fostering a culture of continuous improvement and innovation.

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

• Bachelor's & Master's Degree in computer science, Data Science, or a related field
• 8+ years of experience
• Oral and written proficiency in English required

What Sets You Apart

• Over 3 years 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.