Description
AI Accountant (AiA) is building an AI-native platform transforming accounting and bookkeeping workflows. We simplify compliance, automate financial operations, and help businesses operate with clarity and confidence.
Backed by global investors like Y Combinator, we are scaling fast and building a team that combines speed, ownership, and sharp execution.
We’re not building a feature company. We’re building a category.
Our Mission & Vision
Mission: Simplify and automate financial workflows using AI to empower modern businesses.
Vision: Become the most trusted AI-driven financial operating system globally.
Job Summary:
We are looking for a highly skilled Senior AI Engineer to lead the development, evaluation, deployment, and continuous improvement of AI systems across our product ecosystem. This role will own AI models and new AI initiatives end-to-end, with a strong focus on production-grade LLM applications, multi-agent systems, automation workflows, and model lifecycle management.
The ideal candidate combines deep expertise in modern AI/LLM technologies with strong software engineering fundamentals and experience scaling AI systems in production environments.
Requirements
Experience
7+ years of experience in AI/ML, Machine Learning Engineering, Applied AI, or related roles.
Proven experience building and shipping AI products into production at scale.
Experience owning AI systems end-to-end, from design through deployment and monitoring.
Technical Skills
Strong Python programming skills and solid software engineering fundamentals.
Hands-on experience with LLM APIs, Agentic Systems, LangGraph, RAG, MCP, and LLM Evaluation frameworks.
Experience designing deterministic and semi-deterministic testing frameworks for AI systems and workflows.
Strong knowledge of traditional Machine Learning models, data analysis, debugging, SQL, APIs, JSON, and workflow observability.
Experience with LLMOps/MLOps practices, including model deployment, monitoring, evaluation, and lifecycle management.
Key Responsibilities:
AI Model Ownership
Own the complete lifecycle of AI models from experimentation to production deployment, monitoring, evaluation, and optimization.
Improve existing AI systems by enhancing accuracy, reliability, latency, and scalability.
Drive new AI initiatives from concept to production, collaborating closely with product, engineering, and business teams.
LLM & Agentic Systems Development
Design, build, and optimize LLM-powered applications and agent-based workflows. Develop (RAG) pipelines and knowledge retrieval systems.
Build robust orchestration frameworks using LangGraph and related agent frameworks.
Evaluation & Quality Engineering
Design deterministic and semi-deterministic evaluation frameworks for LLMs and agent systems.
Build automated testing frameworks for prompts, workflows, agents, and model outputs. Analyze model failures and implement improvements through structured evaluation cycles.
MLOps / LLMOps
Develop and maintain scalable LLMOps infrastructure and workflows. Implement monitoring, observability, experimentation, versioning, and deployment processes.
Optimize AI systems for performance, reliability, and cost efficiency.
Drive best practices around model governance, testing, and release management.
What Success Looks Like in this role:
AI models consistently meet defined quality, latency, and reliability benchmarks.
New AI initiatives are delivered from concept to production with measurable business impact.
Robust evaluation and testing frameworks are established across AI systems.
Production AI systems scale efficiently while maintaining performance and cost effectiveness.
Accounting and finance automation capabilities are significantly enhanced through AI innovation.
Benefits
Our Way of Life — The AiA Values
C – Collaborate to Win
We win together, not alone. We listen, challenge with care, and move as one team. Speed and success come from trust and cross-functional teamwork.
E – Elevate the Bar
“Good e