2026 AI Summer Intern
About The Position
At EisnerAmper, we look for individuals who welcome new ideas, encourage innovation, and are eager to make an impact. Whether you’re starting out in your career or taking your next step as a seasoned professional, the EisnerAmper experience is one-of-a-kind. You can design a career you’ll love from top to bottom – we give you the tools you need to succeed and the autonomy to reach your goals. Join the EisnerAI team, a fast-growing organization within EisnerAmper that is redefining how professional services harness the power of Artificial Intelligence. As an AI Engineering Intern specializing in MLOps and AIOps, you will play a critical role in deploying, operating, and optimizing enterprise-grade AI solutions built on Azure, collaborating closely with AI Developers, Product Owners, Governance leaders and Cloud Architects to ensure these systems are reliable, scalable, and cost-effective across tax, audit, advisory, and client-facing applications. This role goes beyond traditional DevOps—it’s about engineering AI into the fabric of enterprise operations, enabling secure, observable, and governed machine learning deployments that deliver real business impact. What it Means to Work for EisnerAmper: You will get to be part of one of the largest and fastest growing accounting and advisory firms in the industry You will join a culture that has received multiple top “Places to Work” awards We believe that great work is accomplished when cultures, ideas and experiences come together to create new solutions
Requirements
- Currently enrolled or recently graduated with a Bachelor’s degree in Computer Science or a related field.
- Must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
- Have the availability to work in-office 3 days a week (Monday – Friday; 8:30 am – 5:30 pm).
Nice To Haves
- Previous consulting internship experience with a strong bias for action.
- Workflow Design: Prompt flow, automation pipelines, and human-in-the-loop systems
- Post-Training Techniques: Fine-tuning, instruction tuning, RLHF, and domain adaptation
- Azure DevOps, App Insights, Log Analytics, Key Vault, and Managed Identity integration.
- Tools for inference performance testing and profiling (e.g., locust, K6, or custom scripts).
- Model Evaluation: Performance metrics, benchmark development, and A/B testing frameworks
- Understanding of model observability, telemetry, and incident response for AI systems.
Responsibilities
- Deploy and monitor AI models across Azure services with robust telemetry for performance, drift, and availability.
- Manage model upgrades including APIs and UIs with structured rollout, version control, and rollback support.
- Optimize performance and cost through testing, profiling, and tuning of inference infrastructure and pipelines.
- Implement MLOps pipelines for continuous integration, deployment, and lifecycle management using Azure ML and GitHub Actions.
- Ensure compliant change management for all AI-related deployments, with auditability, security, and governance controls.
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What This Job Offers
Career Level
Intern
Number of Employees
1,001-5,000 employees
