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.

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

• Apply systems engineering principles to design integrations that are modular, observable, and easy to maintain.

• 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.

XML job scraping automation by YubHub

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.

Key Responsibilities:

• 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:

• Apply systems engineering principles to design integrations that are modular, observable, and easy to maintain.
• 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.

XML job scraping automation by YubHub