Sr. AI Engineer

FinQuery•Atlanta, GA

About The Position

The FinQuery AI team builds and owns the AI and machine learning services that power features across our suite of accounting and finance software products. We develop the shared platform and tooling that other engineering teams rely on to ship AI features and work across the company on AI product design. This is an individual contributor role for a senior AI engineer ready to own complex, production AI systems from prototype through deployment.

Requirements

3+ years of professional software or AI/ML engineering experience with a focus on production LLM services
Proven track record of developing, running, and evaluating autonomous agents in a production environment
Strong Python proficiency with the ability to write clean, maintainable, and reliable code
Solid understanding of agent architectures and the ability to resolve system failures under time pressure
Knowledge of performance optimization, including latency, throughput, and cost management for AI services
Familiarity with data privacy and compliance considerations for systems handling sensitive financial data
Experience with various LLM models (OpenAI, Anthropic, open-source) and modern AI development tools
Ability to communicate complex technical concepts and tradeoffs to both technical and non-technical partners
Minimum of a four-year degree in Computer Science, Engineering, Mathematics, or equivalent experience

Nice To Haves

Full end-to-end AI/ML lifecycle experience including ideation, deployment, and maintenance at scale
Practical experience with the AWS AI/ML stack and document processing using LLMs
Experience mentoring other engineers and familiarity with uv for Python project management

Responsibilities

Build and scale production LLM-powered services, including context engineering, prompt design, and agentic workflows
Develop and operate autonomous AI agents, owning the full lifecycle from prototype to ongoing maintenance
Design and execute evaluations for AI/ML systems to drive architectural decisions and share findings cross-functionally
Contribute to shared AI platforms and internal tooling that enable other teams to deploy and monitor AI features
Write production-grade Python code designed for long-term extensibility and team ownership
Serve as a technical consultant to product and UX teams on AI feasibility, tradeoffs, and architecture
Monitor and optimize AI/ML services for latency, cost-efficiency, and high-volume throughput
Troubleshoot production system health and identify solutions to reduce technical debt and performance degradation
Design AI capabilities with strict adherence to data privacy and compliance standards for financial data
Stay current on emerging AI/ML techniques to raise the collective engineering standard of the team