Define and execute the roadmap for agentic AI and digital worker solutions in Finance, aligning with business priorities and emerging technologies.
Identify and prioritize high-value use cases, translating ambiguous business challenges into measurable outcomes
Lead cross-functional teams, collaborating with Finance, Product, Engineering, and Operations to deliver scalable, production-grade AI solutions.
Architect, build, and scale LLM agents for finance workflows using advanced techniques such as LangGraph, retrieval augmented generation (RAG), multi-agent orchestration, tool use, and multi-step reasoning
Oversee the development of robust data and inference pipelines in Python and SQL; integrate agents with APIs, microservices, BI/reporting tools, and cloud platforms (AWS, Azure, GCP)
Leverage vector databases, embeddings, and distributed compute frameworks (e.g., Databricks, Snowflake, PySpark) for efficient retrieval and performance optimization.
Drive research and development initiatives, exploring Gen AI, Agentic AI, and autonomous workflow patterns.
Mentor and upskill teams; deliver enablement materials, documentation, and best practices for AI adoption.
Foster a culture of innovation, experimentation, and continuous improvement.
Translate model outputs into user-friendly insights and analytics for end users, enabling data-driven decision making.
Communicate complex technical concepts to senior stakeholders; deliver compelling data visualizations and narratives. Required qualifications, capabilities and skills
8+ years in data/ML roles, including 4+ years building and operating production ML applications; hands-on experience with LLMs.
Strong Python and SQL;
Practical knowledge of RAG, prompt engineering, fine-tuning, function/tool calling, and vector stores.
Experience with cloud platforms (e.g., AWS, Azure, or GCP) and modern data stacks (e.g., Databricks or Snowflake).
Familiarity with LLM frameworks and orchestration (e.g., LangChain or LlamaIndex) and REST/GraphQL API design.
Proficiency in analytics and applied statistics; ability to design experiments and evaluate business impact.
Excellent communication and stakeholder management; comfort working across Finance, Technology, and Operations. Preferred qualifications, capabilities and skills
Experience building.

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