Lead AI Solution Architect for Enterprise Platform

EisnerAmper LLP; Conshohocken (Montgomery County); On-site; USD 150,000 - 230,000; Full time

Job summary

EisnerAmper is seeking multiple AI Solution Architects to lead development on the EisnerAI Platform, architecting and implementing the technical framework that powers intelligent solutions across tax, audit, outsourcing and advisory services.

You will drive seamless integration across data management, governance, AI, agents and applications, while advancing model evaluation, deployment, and post-training optimization for custom LLMs and AI agents.

Qualifications

• Bachelor's degree in CS, engineering, data science, or related field.
• 6+ years of software development with 4+ years in AI/ML architecture role.
• 3+ years of hands-on experience with Azure cloud services and enterprise-scale solutions.

Responsibilities

• Ensure seamless integration across the platform's Data Management, Governance, AI, Agents and Application layers.
• Drive model evaluation frameworks and post-training optimization for custom LLMs and AI agents.
• Lead technical development of the EisnerAI Agent Workforce such as TaxPrepBot, ComplianceRiskBot, DocIntake, and M&A Targeting Bot.
• Design agentic frameworks supporting initiation, execution, state management, logging, and error handling.
• Ensure adherence to data governance policies and responsible AI practices.

Skills

Python

C#

JavaScript/TypeScript

SQL

AI/ML architecture

Education

Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field

Master's degree preferred (CS/AI/MBA with technical focus)

Tools

Azure cloud services

Azure AI Foundry

Azure OpenAI Service

Azure ML (MLOps)

Azure Databricks

Job description

EisnerAmper is seeking multiple AI Solution Architects to lead development on the EisnerAI Platform, architecting and implementing the technical framework that powers intelligent solutions across tax, audit, outsourcing and advisory services.

You will drive seamless integration across data management, governance, AI, agents and applications, while advancing model evaluation, deployment, and post-training optimization for custom LLMs and AI agents.