Cloud Devops Architect

Job DescriptionKey Responsibilities1. Multi-Cloud Architecture & Governance • Define and implement cloud-agnostic architecture patterns across AWS and GCP • Standardize GCP governance aligned to AWS controls • Establish reusable reference architectures for data, AI, and infrastructure • Promote abstraction via: o Containers (Kubernetes) o APIs o Infrastructure as Code (Terraform)2. Hands-On Enablement (POCs & Pipeline Delivery) • Build proof-of-concept solutions to validate architecture patterns • Develop and optimize data pipelines and integrations across systems (ServiceNow, Apptio, Jira) • Implement AI-enabled workflows (model integration, automation) • Provide hands-on support to delivery teams to accelerate adoption • Translate architecture into working, scalable solutions3. AI Integration & MLOps Enablement • Design and implement AI-ready pipelines (structured + unstructured data) • Support: o Model integration into enterprise workflows o MLOps lifecycle enablement (CI/CD, monitoring, governance) o AI tool/vendor evaluation • Mature organization from: o POCs → Embedded AI → Governed enterprise AI4. Data Architecture & Integration (CMDB/APM-Aligned) • Architect data flows integrating: o ServiceNow (CMDB/APM) o Apptio (cost transparency) o Jira (delivery data) • Address key challenges: o Data latency o Data duplication o Cost visibility gaps • Enforce system-of-record and data ownership principles5. Governance & FinOps (Advisory + Enablement) • Define standards for: o Cloud cost optimization (FinOps) o AI governance and lifecycle management o Data quality and pipeline SLAs • Support KPI transparency: o Cloud cost per application o Data pipeline reliability o AI ROI • Guide teams while enabling them through working solutions6. Platform Strategy & Shared Services Leadership • Act as a central architecture leader and enabler • Support teams through: o Architecture reviews o POC delivery o Design guidance • Build reusable enterprise assets: o Patterns o Templates o Integration frameworks Required Experience7+ years in cloud architecture, data engineering, or infrastructure • Proven experience in multi-cloud environments (AWS + GCP) • Demonstrated ability to: o Design architecture and deliver working solutions o Build data pipelines and integrations • Strong experience with: o Python, SQL o ETL/ELT pipelines o Infrastructure as Code (Terraform preferred) o Containers (Kubernetes) AI & Modern Architecture RequirementsHands-on experience with: o AI/ML integration into enterprise pipelines o MLOps or AI lifecycle tooling • Experience evaluating and implementing: o AI platforms o Automation tooling Preferred ExperienceServiceNow CMDB/APM integration • Apptio (cost allocation / FinOps) • Experience solving: o Cross-system duplication o Data lineage challenges • Exposure to Generative AI integration Success Metrics (Aligned to Your KPIs)Reduction in cloud cost per application • Improvement in pipeline SLAs • Reduction in duplicate data/integrations • Increase in production AI-enabled workflows • Adoption of multi-cloud architecture standards • Number of successful POCs transitioned to production