GTM AI Engineering Intern

About G2’s Intern Program

G2’s Intern Program is back for its 6th amazing summer! You’ll work closely with leaders and peers throughout the 10 weeks- focusing on making a real impact on G2, with a final presentation about your work to our Executive & Senior Leadership Teams. We’ve packed the summer full of professional development and social events to lay the foundation for your career, ensuring you’ll have a balanced experience over the course of the 10 weeks.

G2 Intern Program Details

• 10 weeks: June 1 - August 7
• Hybrid: Tuesday through Thursday required in-person at our Downtown Chicago HQ
• Pay: $24/hour; 40 hours a week

About The Role

We're seeking a technical engineering intern to help build foundational infrastructure for our AI initiatives in our GTM Teams by creating a comprehensive inventory of AI assets and contributing to the development and deployment of AI agents across the organization. This role requires hands-on experience building AI solutions and the ability to bridge technical implementation with business impact.

As a member of the team this summer, you’ll focus on:

• AI Asset Inventory & Documentation
  • Conduct technical stakeholder interviews across the GTM teams to identify and catalog existing AI models, agents, and automation tools
  • Document current use cases, architectures, input/output specifications, and business outcomes for each AI asset
• AI Agent Development & Deployment
  • Build and deploy AI agents for high-priority use cases (e.g., customer re-engagement, account intelligence, review analysis)
  • Design agent workflows, prompts, and decision logic for production use cases
  • Implement integrations between AI agents and existing systems (CRMs, data platforms, communication tools)
  • Test, iterate, and optimize agent performance based on business outcomes
  • Establish deployment patterns and best practices for scaling agents across teams
• Use Case Analysis & Technical Recommendations
  • Analyze patterns across documented use cases to identify opportunities for consolidation or expansion
  • Evaluate technical feasibility and architecture requirements for new AI applications
  • Assess which assets could be standardized and shared across teams
• Knowledge Sharing
  • Develop technical documentation and presentation materials summarizing findings
  • Create a centralized knowledge base for AI assets, agents, and implementation patterns
  • Present insights and demos to leadership and cross-functional teams
  • Contribute to strategic planning for AI architecture and governance

Minimum Qualifications:

We realize applying for jobs can feel daunting at times. Even if you don’t check all the boxes in the job description, we encourage you to apply anyway.

• A rising Junior or Senior at an accredited college or university pursing a degree in Computer Science, Software Engineering, AI/ML, or related field
• Hands-on experience building and deploying AI agents or automation workflows
• Strong programming skills (Python preferred) with experience using APIs and SDKs
• Familiarity with LLM platforms (OpenAI, Anthropic, etc.) and prompt engineering
• Experience with workflow automation tools and orchestration frameworks
• Strong written and verbal communication skills for technical and non-technical audiences
• Ability to translate technical capabilities into business value
• Self-starter comfortable with ambiguous, exploratory projects

What You'll Learn

• How enterprise organizations evaluate, implement, and scale AI initiatives from prototype to production
• AI agent architecture patterns and deployment strategies for business operations
• Practical application of AI in customer success, sales, and marketing operations
• Change management and stakeholder alignment for emerging technology adoption
• Strategic planning and resource allocation for AI investments
• Cross-functional collaboration in a fast-paced B2B environment

Project Outcomes

By the end of this internship, you will have:

• Created a comprehensive technical inventory of AI assets with standardized documentation and architecture diagrams
• Built and deployed 2-3 production AI agents solving real business problems
• Conducted 15-20 stakeholder interviews across departments
• Identified 3-5 high-priority opportunities for AI expansion or consolidation
• Developed technical recommendations for AI governance, agent management, and infrastructure
• Presented findings and demos to leadership with actionable strategic recommendations