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 recent 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 (Hyderabad)
🏢 PwC Acceleration Center India
📍 Hyderabad