PWC - Agentic AI & Automation Lead- Manager

Role: 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.

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

• 9–15 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 executi…