AI/ML Cloud Architect

Position Overview : As a AI/ML Cloud Architect at ValueLabs , you wll play vital role in conceptualizing , designing , amd implementing holistic and intuitive solutions for complex enterprise applications.Leveraging your expertise in user-centric design principles and your proficiency in cutting-edge technologies, you will collaborate with cross-functional teams to deliver exceptional user experiences across our suite of CRM an HR software products.

Key Responsibilities:
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Cloud architecture, DevOps, or full-stack engineering
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Applying AI/ML in software engineering workflows
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Deep experience with:
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AWS, GCP, Azure
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Terraform, Helm, Kubernetes
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CI/CD (ArgoCD, Jenkins, Tekton)
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Observability (Prometheus, OpenTelemetry, ELK)
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Full-stack development (Node.js, Python, React/Vue)
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Proven ability to integrate or build AI-enhanced developer tools
Qualifications:
Key Responsibilities
AI-Augmented Software Development
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Integrate LLMs into IDEs and CI/CD pipelines for:
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Code generation (TypeScript, Golang, Python)
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API scaffolding (REST, GraphQL)
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Unit, integration, and security test creation
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Code refactoring and documentation
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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
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Automate test case generation for APIs, microservices, and infrastructure.
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Use AI to generate test data, assess test coverage, and recommend improvements.
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Implement AI-based load testing pattern generation and test output analysis.

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

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

Security & Compliance Automation
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Integrate AI tools for CVE detection, patch generation, and IaC hygiene.
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Translate compliance requirements into policy-as-code using NLP.
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Align infrastructure with AWS GovCloud and single-account models.

Documentation & Knowledge Management
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Use AI to generate and improve:
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Architecture and design docs from code
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Microservice documentation for reuse and onboarding
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Release notes, training labs, and customer-facing documentation

Cross-Functional Collaboration
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Partner with engineering, QA, SRE, and documentation teams to align AI initiatives.
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Collaborate with other BUs to adopt or extend shared LLMs and AI tools.
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Lead POCs, benchmarks, and production rollouts of AI-driven workflows