About this role
Summary: Designs, builds, and operates AI solutions such as LLMs, RAG applications, and intelligent agents for Finance. Works with cross-functional teams to deliver trusted, scalable, and governed AI capabilities.
Lead AI/ML Engineer, Enterprise Finance
We are seeking a Lead AI/ML Engineer to design, build, and operationalize AI capabilities that drive decision-making, productivity, and innovation across the Finance organization.
In this role, you will lead the development of AI-powered solutions including large language model (LLM) applications, retrieval-augmented generation (RAG), intelligent agents, classification models, summarization workflows, and decision-support tools. You will work closely with Finance, Data Engineering, Architecture, Security, and Responsible AI teams to deliver trusted, scalable, and governed AI solutions. The role aligns with enterprise AI/ML engineering practices and AI productization standards
Key Responsibilities:
Design AI solution architectures aligned with business objectives, risk tolerance, and governance requirements.
Build and deploy LLM workflows, prompt orchestration chains, agent-based solutions, RAG applications, model integrations, classification models, and decision-support capabilities.
Develop prompt strategies, grounding approaches, tool-calling patterns, guardrails, and fallback mechanisms to improve solution quality and reliability.
Partner with Data Engineering teams to ensure access to trusted, governed, and high-quality data sources.
Establish evaluation frameworks, test datasets, scoring rubrics, and model-performance benchmarks.
Monitor and optimize output quality, relevance, accuracy, consistency, latency, cost efficiency, and hallucination risk.
Conduct failure-mode analysis and implement continuous improvements to AI systems.
Collaborate with Quality Engineering, Responsible AI, Security, Privacy, Compliance, and Enterprise Architecture teams.
Document AI solution behavior, limitations, controls, and operational monitoring requirements.
Support production readiness through observability, monitoring, model evaluation, and drift detection practices.
Required Qualifications:
Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field; equivalent experience considered.
Significant experience in software engineering, machine learning, or AI engineering.
Hands-on experience building and deploying generative AI and LLM-based applications in production environments.
Experience with prompt engineering, RAG architectures, AI agents, model evaluation, and operational AI governance.
Strong understanding of cloud-native architectures, APIs, data platforms, and scalable software systems.
Knowledge of Responsible AI, security, privacy, and compliance practices.
Proven ability to work effectively with technical and business stakeholders.
Preferred Qualifications:
Experience supporting Finance, FP&A, Accounting, Treasury, Tax, or other enterprise finance functions.
1-2+ years in developing and deploying agentic AI solutions.
Familiarity with AI observability, model monitoring, vector databases, semantic search, and knowledge retrieval systems.
Experience leading enterprise AI products from design through production support.