IN_Manager_GenAI team leads_Alliances_IFS_Gurgaon/Bengaluru

Job Description & Summary : As a Manager in the GenAI Experience Lab, you will lead a team that designs and builds generative AI solutions for clients in our priority sectors and competencies. You will own the technical quality of what the team delivers, work directly with clients to define and shape solutions, and run workshops and upskilling sessions. You will stay close enough to the technology to guide design and review the team's work. Proofs of concept support this work but are not the main output. PwC India's GenAI Experience Lab designs and builds generative AI solutions for clients in the sectors and competencies the firm focuses on. We also run workshops to train client and internal teams on GenAI, and build proofs of concept to test ideas before they are taken forward. Job Position Title : IN_Manager_GenAI team leads_Alliances_IFS_Gurgaon/Bengaluru Responsibilities : • Lead the design and build of GenAI solutions aligned with our priority sectors and competencies, owning delivery quality, estimates, and timelines. • Lead and mentor a team of 4–8 GenAI developers and engineers across multiple projects. • Work with clients to identify and size use cases, run discovery workshops, and shape solutions. • Design solution architectures for GenAI applications (chatbots, copilots, multi-agent systems, and agent workflows) and guide the team on patterns and standards. • Apply advanced RAG (hybrid search, query rewriting, re-ranking, GraphRAG, agentic RAG, HyDE) and agent patterns (Plan-and-Execute, supervisor/worker, hierarchical agents, human-in-the-loop). • Set up LLMOps practices: CI/CD for prompts and agents, evaluation gates, model registry, drift detection, and cost tracking. • Put evaluation, safety, and guardrails in place (RAGAS, DeepEval, TruLens, LangSmith, Azure AI Content Safety, Guardrails AI, NeMo Guardrails), along with responsible-AI controls. • Review code, unblock the team, and contribute to solution builds where needed. • Build proofs of concept where they help test ideas and support client decisions. • Design and run client workshops and internal upskilling; build reusable accelerators and a shared demo library. • Work with senior leadership on proposals, SOWs, and client relationships. • Make sure solutions account for scalability, security, and the path to production. Mandatory skill sets: • ~7–10 years overall, with substantial hands-on and leadership experience in GenAI, AI / ML, or related delivery. • A track record of leading technical teams and delivering multiple projects in parallel. • Strong technical depth in GenAI (LLMs, RAG, agent systems, prompt engineering, and the wider GenAI stack), enough to guide architecture and review work. • Solid full-stack and cloud skills, with strong knowledge of at least one cloud platform and working knowledge of the others. • Strong client-facing skills: communication, stakeholder management, workshop facilitation, and presenting work clearly. • Business judgement to connect technology to business value, cost, and adoption. Preferred skill sets : • Experience taking GenAI systems from prototype to production with SLAs and cost controls. • Familiarity with knowledge graphs, multimodal solutions, and LLMOps / FinOps for GenAI. • Consulting background and experience in regulated industries. • Cloud certifications (Azure AI Engineer, AWS ML Specialty, GCP ML Engineer). Years of experience required : 7–10 years Education qualification : • <B.Tech> in Computer Science / IT (preferred). • An MBA in a related field from a Tier-1 or Tier-2 institute (for example IIM A/B/C/L, ISB, XLRI, FMS, MDI) is an added advantage. • Experience in a consulting or client-facing delivery role, and a track record of upskilling teams Tech Stack You'll Work With · Cloud & AI Platforms : Azure (OpenAI, AI Foundry, AI Search), AWS (Bedrock, SageMaker), GCP (Vertex AI, Gemini, Agent Builder) · LLMs & SLMs : GPT-4o/5, Claude Opus/Sonnet 4.x, Gemini 2.x, Llama 3.x, Mistral / Mixtral, Phi, Qwen, DeepSeek, Command R+ · Agent Frameworks : LangGraph, LangChain, LlamaIndex, CrewAI, AutoGen, Semantic Kernel, MCP · RAG & Retrieval : Hybrid + BM25, query rewriting, re-ranking (Cohere, BGE), GraphRAG, agentic RAG, HyDE · Vector & Graph : Pinecone, Weaviate, Qdrant, Milvus, pgvector, Azure AI Search; Neo4j, Neptune · Multimodal : Vision (GPT-4o, Gemini, Claude), speech (Whisper, Azure Speech), document AI (Document Intelligence, Textract) · DevOps & Infra : Docker, Kubernetes, Terraform, GitHub Actions / Azure DevOps, Airflow · Responsible AI : PII redaction, prompt-injection defence, output filtering, lineage / audit; DPDP Act, NIST AI RMF, EU AI Act awareness