Agentic AI & Automation Lead

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.
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.