AI Engineer - Agentic AI

About the job

About the Role: AI engineer -- Agentic AI Join our AI engineering team building the next generation of Gartner's AI platform. You'll tackle challenges at the intersection of search, reasoning, and knowledge representation - building systems that need to be both highly accurate and explainable. This is production engineering at scale: real users, measurable business impact, and technical problems that don't have textbook answers. If you're excited about building AI that actually works in the real world, not just in demos, this is the role. What You'll Do: • AI systems that connect users with the right information, expertise, or insights at scale -- handling tens of thousands of requests with high accuracy and explainability requirements. • Intelligent architectures that combine retrieval, reasoning, and decision-making -- orchestrating multiple AI capabilities to solve complex matching and recommendation problems. • Knowledge representation systems that capture relationships between entities, discover patterns, and enable sophisticated queries beyond simple similarity search. • Production ML pipelines that handle real-world constraints: limited labeled data, noisy inputs, evolving requirements, and the need for both speed and accuracy. • Evaluation and optimization frameworks to continuously improve AI system performance -- measuring what matters and iterating based on real user outcomes. • Scalable deployment infrastructure that takes AI from prototype to production, serving live traffic with reliability and cost-efficiency. What You'll Need: • 2-4 years building ML/NLP systems with real-world deployment. • Strong Python + experience with modern LLM frameworks (LangChain, LangGraph, or similar). • Built or deployed RAG systems beyond tutorials. • Worked with graph databases or knowledge graphs. • Shipped APIs serving real traffic. • Comfortable with ambiguity - we're solving new problems, not following playbooks. Good to Have: • Built multi-agent systems that actually worked in production. • Experience with context optimization for LLMs (prompt engineering, caching). • Knowledge of graph traversal algorithms (PageRank, shortest path, etc.). • Worked on sparse/limited labeled data problems. • Contributed to open-source AI projects.

Requirements

• AI systems
• Python
• LLM frameworks
• RAG systems
• APIs

Preferred Technologies

• AI systems
• Python
• LLM frameworks
• RAG systems
• APIs

About the company

Gartner, Inc. (NYSE:IT) is a global research and advisory firm providing expert analysis and insights to help leaders shape the future. Since its founding in 1979, Gartner has grown to 21,000 associates globally who support 14,000 client enterprises in 90 countries and territories.