Jobs and Careers

AI Solution Architect - Enterprise Intelligence

Electronic Arts

United Statesfull_timeVerifiedPosted 6 Aug 2026

About the role

General Information

Locations: Austin, Texas, United States of America

Role ID

215332

Worker Type

Regular Employee

Studio/Department

CT - Data & Insights

Work Model

Hybrid

Description & Requirements

Electronic Arts creates next-level entertainment experiences that inspire players and fans around the world. Here, everyone is part of the story. Part of a community that connects across the globe. A place where creativity thrives, new perspectives are invited, and ideas matter. A team where everyone makes play happen.

The Enterprise Intelligence (EI) team transforms data into actionable insights that power EA. We guide strategy and governance, build data and AI solutions, and ensure personalization experimentation and analytics; partnering across teams to improve decisions and create more meaningful player experiences.

The AI Solutions Architect will be part of EI team reporting to the Director of AI/ML. This is a hybrid remote/in-office role.

Responsibilities

Design, develop, and deploy AI-powered analytics products applying LLMs, Generative AI, ML Models and modern data infrastructure.
Build end-to-end AI solutions, including prompt engineering, fine-tuning, and agentic system design using frameworks such as LangChain and LangGraph.
Architect scalable backend services and serverless applications using Python on AWS or GCP.
Integrate graph databases, vector databases, and data warehousing technologies to help intelligent data-driven applications.
Collaborate with data scientists, engineers, and product teams to translate distributed systems and gaming-related problems into AI-driven Analytical products.

Qualifications

5+ years of experience in software engineering or applied AI development, with at least 3+ years focused on LLM or Generative AI applications.
Experience with Python backend development and cloud-native architectures (AWS Lambda, GCP Cloud Run).
Hands-on expertise with LLM frameworks such as LangChain, LangGraph, or OpenAI function calling, including prompt design, fine-tuning, and agent orchestration.
Knowledge of AWS and cloud technologies, including building, deploying, and maintaining scalable data and application pipelines using services such as S3, Lambda, Glue, EMR, Redshift, ECS, EKS, and SageMaker.
Experience with data systems, including SQL and NoSQL databases, data warehousing, vector databases and graph databases (e.g., Neo4j).
Familiarity with frontend technologies (React, TypeScript) and business intelligence tools for building full-stack analytical applications.
Experience with software engineering best practices, including unit testing, Git-based version control, and CI/CD pipelines.