AIML & SRE Devops Manager / Architect

Key Responsibilities

AI-Augmented Software Development

Integrate LLMs into IDEs and CI/CD pipelines for:
Code generation (TypeScript, Golang, Python)
API scaffolding (REST, GraphQL)
Unit, integration, and security test creation
Code refactoring and documentation
Build AI agents to recommend best practices, detect security flaws, and align with compliance standards (TISAX, SOC, FedRAMP, AWS GovCloud).
AI-Driven Testing & Quality Engineering

Automate 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 Intelligence

Architect AI-enhanced CI/CD pipelines (ArgoCD, Jenkins, Tekton) with predictive deployment analysis and rollback automation.
Use AI to:
Parameterize and refactor Terraform modules
Translate Terraform to CloudFormation
Align infrastructure with AWS WAR, NIST, and Prisma Cloud recommendations
Enable self-healing infrastructure and cost optimization recommendations.
Observability & SRE Automation

Build AI agents to:
Analyze Istio, Prometheus, and logging data
Detect anomalies and correlate events
Recommend or auto-apply fixes
Monitor pipelines and infrastructure for performance, cost, and reliability insights.
Security & Compliance Automation

Integrate 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 Management

Use AI to generate and improve:
Architecture and design docs from code
Microservice documentation for reuse and onboarding
Release notes, training labs, and customer-facing documentation
Cross-Functional Collaboration

Partner 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.

Qualifications

Must-Have

7+ years in cloud architecture, DevOps, or full-stack engineering
2+ years applying AI/ML in software engineering workflows
Deep experience with:
AWS, GCP, Azure
Terraform, Helm, Kubernetes
CI/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 tools
Nice-to-Have

Experience with MLOps platforms (MLflow, SageMaker, Kubeflow)
Familiarity with AI security tooling and compliance automation
Certifications: AWS/GCP Architect, CKA, etc