Electronic Arts

Position Type: Temporary Full-Time

Experience Level: 8+ years of professional systems software engineering experience

About the Team

GameKit Operations (GKO) powers EA's game creation ecosystem through scalable services and thoughtful automation. The GameKit Assistant (GKA) is an AI-driven platform supporting developers and artists across internal tools like Shift, Jukebox, and Perforce, and commercial tools like Jira, GitLab, and Artifactory.

GKA is evolving from a documentation-based chatbot into a Python-powered, context-aware operations assistant delivering real-time insights and automation. We are looking for an engineer with deep Python expertise, systems design experience connecting AI with production systems.

Role Overview

The AI Solutions Engineer will design and implement intelligent integrations that allow GKA to work with both internal and external platforms. You will build Python services, develop orchestration logic through the Model Context Protocol (MCP), and implement automation pipelines that enhance the assistant's ability to provide contextual, data-driven answers. You will be reporting to the Manager, Game Creation Ops.

Responsibilities

• Build scalable Python backend services using FastAPI, Flask, or Django REST Framework.

• Design secure API integrations using OpenAPI/Swagger and OAuth2/JWT standards.

• You will develop MLOps pipelines for model deployment, monitoring, and evaluation using Kubeflow, MLflow, SageMaker, and Terraform.

• Implement and maintain retrieval-augmented generation (RAG) systems with vector databases such as Azure Cognitive Search or Pinecone.

• Build MCP wrappers for GameKit products and prototype integrations with commercial MCPs (GitLab, Jira, Confluence).

• Deploy and monitor services using Kubernetes, Docker, and ArgoCD, with observability through Grafana and Prometheus.

• Apply systems engineering principles to design integrations that are modular, observable, and compliant with EA security policies.

• You will partner with AI, Ops, and Product Engineering teams to define schemas, error models, and testing standards.

Qualifications

• 8+ years of Python engineering experience in backend or systems integration.

• Experience with MLOps, CI/CD, and container orchestration.

• Experience with REST APIs, RAG pipelines, vector databases, and schema-driven architectures.

• Familiarity with Scikit-learn, PyTorch, or TensorFlow for metric integration and model evaluation.

• Bonus: experience with MCPs, OpenAI APIs, or enterprise automation tools such as ServiceNow or Power Automate.

Who You Are: A systems-minded Python engineer who enjoys solving complex integration challenges, building reliable automation pipelines, and enabling AI systems to operate seamlessly across platforms.