Sr. AI Systems Engineer at FinQuery

Job Description

FinQuery stands at the forefront of accounting automation, driven by a deep specialty in contract-driven accounting. Our AI-enabled platform transforms how controllers and finance teams operate, seamlessly managing and accounting for the complex financial contracts—like leases, prepaids, and accruals—that are the backbone of modern business. We are not just a software provider; we are the unified subledger that eliminates time-intensive, error-prone technical accounting workflows, ensuring financial reports are accurate and empowering our customers to focus on strategic, high-value tasks.

FinQuery is the global leader in lease accounting (as recognized on G2.com) and serve more than 8,500 customers worldwide. Our growth trajectory has been consistently validated by the Inc 5000, which has recognized us as one of the fastest-growing private companies for five consecutive years.

Solve the Problems That Matter Most

The Sr. AI Systems Engineer is a technically sophisticated individual contributor responsible for designing, developing, and deploying agentic AI tools and autonomous workflows that eliminate manual bottlenecks and unlock operational leverage across the organization. This role sits at the intersection of software engineering and AI/LLM architecture—translating complex business processes into intelligent, self-executing systems. The AI Systems Engineer brings deep engineering instincts to a fast-evolving toolset, building solutions that are not only functional but observable, maintainable, and aligned to strategic priorities.

How You'll Make an Impact:

Design and develop agentic AI workflows using LLM orchestration frameworks while implementing planning, reflection, and tool-use patterns

Architect and build system integrations connecting AI agents to internal APIs, databases, and third-party SaaS platforms

Develop and maintain Human-in-the-Loop (HITL) checkpoints and escalation logic to surface agentic decisions for human validation

Implement observability and feedback loops within deployed agents to capture performance signals, exception rates, and decision traces

Analyze existing technical workflows to identify automation opportunities and produce engineering-ready specifications

Contribute to documentation standards for agentic systems including technical specs, architecture diagrams, and runbooks

Partner with internal stakeholders to translate operational pain points into technical requirements

Conduct all business in accordance with company policies and procedures

Perform all other duties as assigned

The Expertise You'll Bring:

Education and Experience

Typically requires a minimum of 3 years of related experience with a Bachelor's degree or 2 years with a Master's degree
Preferred 3+ years in software or systems engineering with at least 1 year focused on LLM-based automation or agentic system design
Bachelor's degree in Computer Science, Software Engineering, or a related technical field preferred

Knowledge and Abilities

Proficiency in Python and at least one LLM orchestration framework such as LangChain, LlamaIndex, or CrewAI
Strong API integration skills across REST and GraphQL including comfort with various authentication patterns
Working knowledge of prompt engineering, retrieval-augmented generation (RAG), and tool/function calling patterns
Experience with SQL and NoSQL databases including schema design for agentic memory and state management
Familiarity with cloud environments and deployment patterns for AI workloads
Version control proficiency using Git with disciplined branching and documentation practices
Ability to decompose complex processes into discrete and testable automation components
Experience designing observability into systems including logging, alerting, and performance tracking
Ability to communicate technical architecture and tradeoffs clearly to non-technical stakeholders
Comfort working across functions to gather requirements and iterate on agent behavior