Job Title

Key ResponsibilitiesAI-Augmented Software DevelopmentIntegrate LLMs into IDEs and CI/CD pipelines for:Code generation (TypeScript, Golang, Python)API scaffolding (REST, GraphQL)Unit, integration, and security test creationCode refactoring and documentationBuild AI agents to recommend best practices, detect security flaws, and align with compliance standards (TISAX, SOC, FedRAMP, AWS GovCloud).AI-Driven Testing & Quality EngineeringAutomate test case generation for APIs, microservices, and infrastructure.Use AI to generate test data, assess test coverage, and recommend improvements.Implement AI-based load testing pattern generation and test output analysis.Infrastructure & DevOps IntelligenceArchitect AI-enhanced CI/CD pipelines (ArgoCD, Jenkins, Tekton) with predictive deployment analysis and rollback automation.Use AI to:Parameterize and refactor Terraform modulesTranslate Terraform to CloudFormationAlign infrastructure with AWS WAR, NIST, and Prisma Cloud recommendationsEnable self-healing infrastructure and cost optimization recommendations.Observability & SRE AutomationBuild AI agents to:Analyze Istio, Prometheus, and logging dataDetect anomalies and correlate eventsRecommend or auto-apply fixesMonitor pipelines and infrastructure for performance, cost, and reliability insights.Security & Compliance AutomationIntegrate AI tools for CVE detection, patch generation, and IaC hygiene.Translate compliance requirements into policy-as-code using NLP.Align infrastructure with AWS GovCloud and single-account models.Documentation & Knowledge ManagementUse AI to generate and improve:Architecture and design docs from codeMicroservice documentation for reuse and onboardingRelease notes, training labs, and customer-facing documentationCross-Functional CollaborationPartner with engineering, QA, SRE, and documentation teams to align AI initiatives.Collaborate with other BUs to adopt or extend shared LLMs and AI tools.Lead POCs, benchmarks, and production rollouts of AI-driven workflows.QualificationsMust-Have7+ years in cloud architecture, DevOps, or full-stack engineering2+ years applying AI/ML in software engineering workflowsDeep experience with:AWS, GCP, AzureTerraform, Helm, KubernetesCI/CD (ArgoCD, Jenkins, Tekton)Observability (Prometheus, OpenTelemetry, ELK)Full-stack development (Node.js, Python, React/Vue)Proven ability to integrate or build AI-enhanced developer toolsNice-to-HaveExperience with MLOps platforms (MLflow, SageMaker, Kubeflow)Familiarity with AI security tooling and compliance automationCertifications: AWS/GCP Architect, CKA, etc.