Senior GenAI Architect Agentic AI & LLM Platforms

Price Waterhouse Cooper LLP; Chennai; On-site; INR 4,000,000 - 6,500,000; Full time

Job summary

Price Waterhouse Cooper LLP in Chennai seeks a Senior GenAI Architect/Lead to drive enterprise-wide GenAI adoption and build Agentic AI platforms. You will lead a hands-on team to design scalable, production-grade GenAI systems, collaborating with engineering, product, and leadership.

The role requires 8+ years of experience with 3+ years in GenAI/LLM ecosystems, and strong expertise in RAG, semantic search, and tool integration. The candidate will act as a change agent for GenAI adoption.

Qualifications

• 8+ years of experience with at least 3+ years in GenAI/LLM ecosystems.
• Hands-on experience designing enterprise GenAI architectures and scalable systems.
• Ability to translate business needs into technical designs and roadmaps.

Responsibilities

• Lead a team of GenAI engineers to deliver enterprise GenAI apps and accelerators.
• Design and supervise RAG pipelines, semantic search, and knowledge integration.
• Oversee API, data models, security, observability, and production readiness.
• Drive adoption of GenAI across SDLC and business workflows.

Skills

Enterprise GenAI Architecture

RAG & Semantic Search

Agentic AI

MCP & A2A

Backend APIs

Observability

Data Architecture

Caching

Messaging & Event Processing

Cloud & Model Strategy

LLMOps / MLOps

Security & Responsible AI

Job description

Job Title: Senior GenAI Architect / Lead Agentic AI & LLM Platforms

Location: Chennai

Experience: 8+ years (with 3+ years in GenAI/LLM ecosystems)

Role Overview

• We are looking for a highly experienced, hands-on GenAI Architect/Lead to drive enterprise-wide adoption of Generative AI and building Agentic AI solutions. This role combines deep technical expertise, solution architecture to enable GenAI-led transformation across the SDLC and business workflows.
• You will work closely with engineering teams, architects, product owners, and leadership to design and implement scalable, production-grade GenAI systems, while also acting as a thought leader and change agent for GenAI adoption.

Key Responsibilities

Technical Leadership & Delivery

• Lead a team of GenAI developers and backend engineers to deliver enterprise GenAI applications and accelerators.
• Translate business requirements into technical designs, work breakdown, implementation plans, and engineering tasks.
• Review architecture, code, prompt design, RAG pipeline design, data model, API design, and integration approaches.
• Ensure quality, performance, security, maintainability, observability, cost efficiency, and production readiness.

Advanced RAG, Semantic Search & Knowledge Architecture

• Design and optimize RAG pipelines including ingestion, chunking, metadata extraction, embeddings, vector indexing, hybrid retrieval, re-ranking, citation generation, and answer synthesis.
• Implement semantic search across structured, semi-structured, and unstructured knowledge sources.
• Integrate with enterprise knowledge repositories such as SharePoint, Confluence, S3, Google Drive, databases, ITSM tools, and document management systems.
• Define knowledge freshness, access-controlled retrieval, lineage, deduplication, feedback loops, and retrieval quality improvement mechanisms.

Agentic AI & MCP-based Tool Integration

• Design and implement agentic workflows using LangGraph, LangChain, CrewAI, AutoGen, LlamaIndex, or equivalent.
• Implement tool calling, memory, workflow state, human-in-the-loop approval, API actions, and safe execution boundaries.
• Build or guide MCP server implementations for secure agent-to-tool and LLM-to-enterprise-system integration.
• Evaluate A2A communication patterns for multi-agent collaboration, delegation, and hand-offs where relevant.

Backend, API, Data & Event-Driven Engineering

• Build scalable services using Python, FastAPI, Node.js, Java, or similar technologies.
• Design REST APIs and microservices for LLM orchestration, semantic search, ingestion, agent execution, feedback, and evaluation.
• Use SQL and NoSQL databases for application metadata, session history, audit logs, feedback, configurations, and workflow state.
• Design Redis caching patterns for session state, memory, rate limiting, token optimization, retrieval caching, distributed locks, and workflow continuity.
• Design asynchronous workflows using Kafka, RabbitMQ, Azure Service Bus, AWS SQS/SNS, Google Pub/Sub, or equivalent, including retry, DLQ, idempotency, ordering, and schema management.

Evaluation, Observability, Security & Governance

• Implement LLM tracing, prompt/version tracking, token usage monitoring, latency tracking, cost monitoring, and feedback analytics.
• Define evaluation datasets and metrics for faithfulness, groundedness, answer relevance, context relevance, hallucination rate, toxicity, latency, and cost.
• Use LangSmith, Langfuse, RAGAS, DeepEval, TruLens, Promptfoo, OpenTelemetry, Grafana, Prometheus, ELK, Datadog, or equivalent.
• Implement guardrails against prompt injection, jailbreaks, data leakage, unauthorized tool execution, toxic responses, and privacy violations.
• Support AI-assisted SDLC adoption using GitHub Copilot, Codex, CursorAI, Claude Code, Cline, or similar tools.

Required Skills

• Enterprise GenAI Architecture (Expert)
  • Design and implementation of GenAI reference architectures
  • Platform strategy and governance
  • Reusable accelerators and frameworks
  • Non-Functional Requirements (NFRs)
  • Enterprise-scale AI solution design
• RAG & Semantic Search (Expert)
  • Hybrid Search
  • BM25 Search
  • Vector Databases
  • Embeddings
  • Re-ranking
  • Citations and Grounding
• Agentic AI (Expert)
  • LangGraph
  • LangChain
  • CrewAI
  • AutoGen
• MCP & A2A (Architect-Level Expertise)
  • Model Context Protocol (MCP)
  • MCP Servers and Gateways
• Backend APIs (Strong Hands-on)
  • Python
  • FastAPI
  • Node.js
  • Java
• Observability (Expert)
  • LangSmith
  • Langfuse
• Data Architecture (Expert)
  • SQL & NoSQL Databases
• Caching (Expert)
  • Redis
• Messaging & Event Processing (Expert)
  • Kafka
• Cloud & Model Strategy (Expert)
  • Azure OpenAI
• LLMOps / MLOps / DevOps (Expert)
  • Prompt and Model Versioning
• Security & Responsible AI (Expert)
  • AI Guardrails

Soft Skills & Leadership

• Excellent communication and stakeholder management skills
• Proven ability to drive adoption and influence teams
• Strong problem-solving and architectural thinking
• Experience in mentoring teams and leading technical initiatives