General Information : Locations : Hyderabad, Telangana, India- Role ID

- 213675
- Worker Type
- Temporary Employee
- Studio/Department
- CT - IT
- 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.

Location : Hyderabad | Hybrid Position Type : Temporary Full-Time Experience Level : 8+ years of skilled systems software engineering experience

About the Team : GameKit Operations (GKO) enables EA’s game creation ecosystem through scalable services and intelligent automation. The GameKit Assistant (GKA) is our AI-driven platform that supports developers, artists, and engineers across game creation products like Shift, Jukebox, and Tryouts, along with supporting COTS products like Jira, Gitlab, Artifactory, Perforce.

GKA is evolving from a documentation-based chatbot into a Python-powered, context-aware operations assistant that delivers real-time insights and automation at scale. We are expanding our team with engineers who bring strong Python expertise, an understanding of systems design, and a passion for building the connective tissue between AI and production systems.

Role Overview : We are seeking an AI Solutions Engineer with strong Python expertise to design and implement intelligent integrations for the GameKit Assistant. This role is deeply technical and requires hands-on experience building robust, secure, and maintainable backend services in Python.





You will build the interfaces and orchestration logic that allow GKA to communicate with internal tools and commercial platforms through the Model Context Protocol (MCP) and function-calling frameworks. The position involves designing schema-driven APIs, implementing automation pipelines, and collaborating across teams to deliver context-aware AI functionality.

- Key Responsibilities : Python Engineering :

- Design and implement scalable backend services in Python using frameworks such as FastAPI, Flask, or Django REST Framework .Build and maintain data-access layers, caching mechanisms, and API wrappers that power MCP integrations.- Implement schema validation, error handling, and retry logic for reliable automation.- Write high-quality, tested, and maintainable code with strong adherence to EA security and performance standards.

**-

- MLOps and Pipeline Engineering**
- Implement MLOps pipelines for model training, deployment, and monitoring using tools such as Kubeflow, MLflow, SageMaker, and Terraform .Integrate with existing Kubernetes and Docker infrastructure for scalable AI service orchestration.- Collaborate with AI Engineering to automate model evaluation and continuous improvement workflows.

RAG and Evaluation Systems

- Implement and maintain retrieval-augmented generation (RAG) systems and internal knowledge bases.Work with vector databases such as Azure Cognitive Search , manage embeddings, chunking, reranking,



and retrieval logic.- Contribute to performance evaluation frameworks for model outputs using Scikit-learn, PyTorch, or TensorFlow for metrics integration (no model training expected).

- AI and MCP Integration :

- Develop and maintain MCP wrappers for key GameKit products (Shift, Jukebox, Perforce).Implement function calling and orchestration logic that connects multiple systems to provide contextual insights.- Prototype integrations with commercial MCPs (GitLab, Jira, Confluence) to validate interoperability.- Contribute to evaluation pipelines to measure assistant accuracy and API reliability.

- Systems and Platform Engineering :Work with ArgoCD, Kubernetes, and Docker to deploy and monitor services.- Implement metrics, logging, and alerting for all automation endpoints using tools such as Grafana and Prometheus .- Ensure integrations comply with EA’s authentication, authorization, and data-governance policies.-

- Participate in system design discussions focused on how to bring models “alive” within production pipelines.- Design end-to-end integrations that bridge AI orchestration, MLOps, and backend infrastructure for reliability and scale. - Collaboration and Enablement :

- Partner with AI, Ops, and Product Engineering teams to define schemas, error models, and test suites.Mentor peers on Python best practices, performance tuning, and secure API design.- Document workflows, integration standards, and technical guidelines for broader adoption.

- Qualifications and Experience :

- 8+ years of experience in Python engineering , with exposure to machine learning and MLOps ecosystems (Kubeflow, MLflow, SageMaker, Terraform).- Advanced understanding of RESTful APIs, OpenAPI/Swagger , and sch

📌 Software Engineer, Ai Solutions (Hyderabad)
🏢 Electronic Arts
📍 Hyderabad