Software Engineer (Agentic AI/Data Engineering)

Job Posting Title:

Software Engineer

About Gartner IT:

Join a world-class team of skilled engineers who build creative digital solutions to support our colleagues and clients.  We make a broad organizational impact by delivering cutting-edge technology solutions that power Gartner.  Gartner IT values its culture of nonstop innovation, an outcome-driven approach to success, and the notion that great ideas can come from anyone on the team. 

About this role:

Join our Agentic AI Applications team to build the next generation of Gartner’s Sales and Service Delivery enablement tools. In this role, you will help architect a sophisticated Agentic AI ecosystem designed to act as a force multiplier for our associates, moving beyond simple chatbots to create context-aware digital partners. You will drive the development of a comprehensive intelligent digital assistant capable of "connecting the dots" between client initiatives, value delivery, past engagements & interactions, and our vast library of expert research. You will engineer solutions that leverage Retrieval-Augmented Generation (RAG), multi-agent orchestration, secure intent recognition and efficient context handling—ensuring our Sales and Service teams have a powerful, intelligent interface to navigate critical business data and be more productive and effective in their client interaction preparation and follow up workflows.  

What you’ll do: 

• Architect & Build Agentic Systems: Design and implement scalable, multi-agent architectures that autonomously retrieve, synthesize, and act upon complex data sets—including client intelligence, strategic priorities, and historical engagement & Interactions logs.

• Develop Advanced RAG Pipelines: Engineer robust Retrieval-Augmented Generation (RAG) solutions that aggregate diverse business intelligence—spanning strategic client priorities, communication history, and value metrics—to ensure the AI possesses a holistic, real-time understanding of the client relationship.

• Orchestrate Complex Workflows: Build the logic that "connects the dots" across disparate systems, enabling the digital assistant to hand off tasks to specialized sub-agents or external APIs.

• Ensure Enterprise-Grade Reliability: Implement rigorous guardrails, security controls, and intent recognition layers to ensure the AI acts safely and accurately when handling sensitive data.

• Scale from Concept to Production: Lead the technical evolution of AI capabilities from experimental POCs to robust, high-availability systems, ensuring the platform scales effortlessly to support thousands of global users.

• Optimize Performance & Cost: Fine-tune LLM interactions and context window usage to balance latency, cost, and response quality, ensuring a seamless real-time experience for Sales and Service users.

• Collaborate & Mentor: Partner closely with Product Managers and Data Scientists to translate high-level business requirements into technical roadmaps, while mentoring junior engineers in best practices for AI application development.

What you’ll need: 

• 2+ years of professional software engineering experience, with a strong track record of shipping production-quality code.

• Proficiency in Python application development, including Fast API, asynchronous programming and performance optimization.

• Hands-on experience building AI applications using orchestration frameworks (specifically LangGraph or LangChain) and implementing Retrieval-Augmented Generation (RAG) using Vector Databases.

• Experience designing and deploying scalable solutions on AWS (e.g., Lambda, ECS/EKS, API Gateway, DynamoDB) and a strong background in distributed systems, APIs, microservices, container orchestration etc.

• Working knowledge of modern frontend frameworks, particularly React, with the ability to understand how backend APIs drive the user interface and an ability to collaborate effectively with Product & Design.

• Experience transitioning complex systems from "Proof of Concept" (POC) to high-availability production environments serving a large user base.

• Strong communication and collaboration skills to work effectively across time zones and global teams.

Good to have:

• LLM Tuning & Evaluation: Experience with prompt engineering strategies, evaluating LLM performance (using frameworks like RAGAS or TruLens), or fine-tu