PwC
Website:
pwc.com
Job details:
About the Role
At PwC, our Managed Services platform helps clients focus on their business while we build, operate, and protect their solutions. Within AI Managed Services, we are transforming Finance and ERP AMS delivery (Oracle & SAP) through LLM-powered agents, RAG workflows, and intelligent automation.
As a Manager – Agentic AI & Automation Lead, you will own the strategy, architecture, and delivery of GenAI and agentic automation solutions across Finance Ops and ERP AMS — and you will build and lead the team that delivers them, including AI Solution Leads and Agentic AI / Automation Engineers. This is a hands-on leadership role: you will set the technical direction, architect end-to-end solutions, and grow a high-performing AI engineering team that ships from POC to production in short sprints.
You will partner with Finance and ERP SMEs to translate business pain points into AI use cases, act as a trusted advisor to senior stakeholders, and establish the reusable frameworks that power our MS AI Factory.
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
- 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.
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