This role is deeply technical and requires hands-on experience building robust, secure, and maintainable backend services in Python....Key ResponsibilitiesPython 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....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.
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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 schema-driven design.
• Proven experience integrating external APIs and designing resilient service-to-service communication.
• Solid understanding of authentication frameworks (OAuth2, JWT) and secure credential handling.
• Experience with CI/CD pipelines , Git, and cloud deployment environments.
• Exposure to observability stacks (Prometheus, Grafana, ELK) and debugging production systems.
• Working knowledge of Docker, Kubernetes , ArgoCD and containerized deployments for ML or AI-based systems.
• Familiarity with RAG architectures , embedding models, and vector databases (e.g., Azure Cognitive Search, Pinecone, Weaviate).
• Awareness of evaluation frameworks such as Scikit-learn, PyTorch, or TensorFlow , with the ability to integrate me...We will also consider employment qualified applicants with criminal records in accordance with applicable law.
