Finance AI Engineer (Consultant)

Overview

In this role you will build production-grade AI applications for finance, using data and engineering to influence cash, profitability, risk and growth. You’ll work within a delivery pod on client data from early engagement, turning finance requirements into observable, secure software and robust user surfaces. You’ll implement agent-based workflows and AI patterns that operate within finance processes, with a focus on reliability, governance and measurable business impact. This is a hands-on, cross-functional position that spans engineering, finance processes and decision-making at senior levels.

Responsibilities

• Build and deploy agentic workflows into live finance processes (reconciliations, journals, exception handling, disputes, collections, variance explanations) with clear acceptance criteria and controlled failure behavior
• Implement LLM application patterns (tool calling, structured outputs, RAG, memory management, multi-agent orchestration, versioning)
• Integrate with ERP/EPM systems, document repositories and workflow tools, including secure APIs, events, identity controls and reusable connectors
• Write evaluations, instrument agent behavior, ensure visibility of failure modes, and maintain observability with automated tests, latency and cost measures
• Create finance user interfaces for review/approval/evidence of agent actions with provenance, citations and accessible error-recovery journeys
• Package and operate solutions using cloud AI services, containers/serverless, CI/CD, infrastructure as code, secrets management, monitoring and rollback
• Collaborate with finance users, process specialists, architects and data engineers to clarify requirements, document procedures and runbooks
• Contribute reusable patterns/assets back to the practice with code, evaluation artifacts and reference implementations

Key requirements

• Strong software engineering discipline with Python and at least one other production language (e.g., TypeScript), version control, testing, CI, secure coding practices, SQL, API design
• Hands-on LLM application development (tool use, structured outputs, retrieval and agent frameworks, embeddings, vector/graph retrieval, prompt/config management)
• Experience with evaluation, observability and LLMOps/AgentOps (versioning, monitoring, release/rollback, quality and cost analysis)
• Understanding of secure enterprise integration and sensitive-data handling (auth, authorization, secrets, logging, PII-aware design)
• Ability to work with non-technical finance users to translate needs into testable requirements and explain options/risks
• minimum 4 years of relevant professional experience

• collaboration with non-technical stakeholders
• clear communication of options, risks and trade-offs
• constructive challenge of assumptions
• Python
• TypeScript or equivalent
• SQL

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