Job Description 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 explicit 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. Cloud, CI/CD & Platform - Cloud AI – Deliver solutions on cloud AI services — Azure OpenAI, AWS Bedrock, and GCP Vertex — optimized for scale, security, and cost. - CI/CD – Oversee CI/CD automation with GitHub Actions, Docker, and Kubernetes for reliable, repeatable deployment. - Automation Tooling – Apply enterprise automation tools (e.G., UiPath, Power Automate, n8n) where they complement agentic solutions. Stakeholder Engagement & Advisory - Collaboration – Collaborate with Finance and ERP SMEs to convert business cases into technical designs and measurable outcomes. - Advisory – Act as a trusted advisor, presenting AI-driven insights and trade-offs to senior stakeholders in clear, non-technical language. Required Skills & Experience - 10–15 years in AI/ML and automation,



including 3–4+ years leading AI engineering teams with proven mentoring and delivery leadership. - Advanced Python with LLM frameworks — LangChain, CrewAI, AutoGen, Semantic Kernel — and hands-on agentic solution building. - Strong experience with LLM APIs (OpenAI, Anthropic, Gemini, Mistral), RAG patterns, vector databases (Pinecone, Chroma, FAISS), embeddings, and LLM fine-tuning. - Experience integrating AI with ERP (Oracle / SAP) and Finance Ops systems via APIs and OIC, with familiarity with Finance data models. - Proven delivery of AI solutions in Managed Services or ERP operations, leading cross-functional projects from POC to production. - Cloud AI services (Azure OpenAI, AWS Bedrock, GCP Vertex) and CI/CD automation (GitHub Actions, Docker, Kubernetes). - LLMOps / MLOps for model, prompt, and agent lifecycle — monitoring, governance, and auditability. - Excellent stakeholder engagement and executive communication, translating AI capability into business value. Preferred / Nice-to-Have Skills - Experience delivering GenAI applications for enterprise Finance / ERP operations at scale. - Exposure to ITSM, AMS, or Finance Managed Services environments and operating models. - Familiarity with enterprise automation platforms (UiPath, Power Automate, n8n). - LLMOps observability tooling (LangSmith, Langfuse, Arize) and evaluation frameworks for GenAI and agents. - AI/ML or cloud certifications (Azure / AWS / GCP). Why This Role Stands Out - Lead the agentic-AI transformation of Finance & ERP Managed Services — a rare blend of solution architecture, hands-on engineering, and team leadership. - Build and grow a GenAI engineering team and shape the reusable AI Factory that scales across engagements. - High-visibility role with direct client and senior leadership interaction across industries.

📌 Agentic Ai & Automation Lead (Bengaluru)
🏢 PwC Acceleration Center India
📍 Bengaluru