PwC - Lead/Manager - Agentic AI & Automation

Agentic AI & Automation Lead

Level: Manager

Experience: 9-14 years

Locations: Bangalore,Hyderabad

Key Responsibilities :

• Agentic AI & GenAI Solution Leadership Solution Architecture - Own end-to-end AI solution architectures for Finance Ops and ERP AMS processes, from use-case shaping through production.

• Agentic Design - Direct the design of LLM and agentic workflows using LangChain, AutoGen, CrewAI, and Semantic Kernel - planning, tool use, memory, and human-in-the-loop controls.

• RAG & Retrieval - Govern RAG pipelines and embedding design using vector stores such as Pinecone, Chroma, or FAISS to deliver contextual, grounded intelligence.

• Prompt & Agent Standards - Set standards for prompt chains and autonomous-agent behavior, ensuring accuracy, governance, and auditability.

• ERP & Finance Integration Integration - Oversee integration of AI solutions with Oracle, SAP, and Finance Ops systems via APIs and OIC.

• AMS Automation - Direct automation of ticket triage, reporting, and communication drafts for AMS teams to reduce manual effort and improve speed and accuracy.

• Domain Alignment - Bring working knowledge of Finance data models and ERP processes to ground solution design in domain reality.

• Team Leadership & Delivery People Leadership - Lead, mentor, and grow a team of AI Solution Leads and Agentic AI / Automation Engineers - setting clear goals, KPIs, and career-development plans.

• Delivery Management - Run delivery in short (6-week) sprints, taking solutions from prototype to production while managing timelines, risks, quality, and client SLAs.

• Coaching - Coach the team on emerging GenAI and agentic techniques; foster a culture of rapid prototyping, experimentation, and continuous learning.

• Cross-Functional Lead - Lead cross-functional AI projects from POC to production, balancing hands-on technical input with delivery oversight.

• MS AI Factory & Reusability Reusable Assets - Define reusability frameworks and common components for the MS AI Factory to accelerate delivery across engagements.

• Capability Building - Contribute accelerators, patterns, and best practices to shared repositories and centers of excellence, and help win and shape new client engagements.

• LLMOps, Governance & Responsible AI LLMOps - Stand up LLMOps for GenAI and agentic workloads - versioning, prompt/agent management, and monitoring for drift, hallucination, latency, and cost.

• Governance & Responsible AI - Embed governance, auditability, guardrails, and Responsible AI (fairness, transparency, security, privacy) into every deployment, aligned to frameworks such as NIST AI RMF and applicable regulations.