AI Platform Adoption Engineer Build

Role & responsibilities

• Lead application onboarding workshops, discover requirements, define rollout plans, track onboarding health, adoption metrics, and self-service maturity.
• Deploy, configure, and operate the platform across AWS/Azure/GCP, including authentication, identity mapping, IAM, and security policy enforcement.[JE1]
• Build and maintain reusable CI/CD pipeline templates with deployment automation, quality gates, SAST/SCA scans, SBOM generation, observability, and DORA metrics instrumentation.
• Integrate client engineering toolchains including Jenkins, GitHub Actions, Azure Pipelines, AWS CodePipeline, GCP Cloud Build, Bitbucket, JIRA, Azure Boards, and ServiceNow.
• Design and govern the platform catalog using catalog YAML, entity models, ownership taxonomy, lifecycle management, dependency mapping, namespace strategy, tags, and registration standards.
• Develop and publish golden-path scaffolding templates, custom Backstage plugins, and TypeScript/Node.js extensions aligned to client engineering standards.
• Enable containerized deployment and platform engineering practices through Kubernetes (EKS/AKS/GKE), container registries, manifest standardization, and deployment automation.
• Implement end-to-end observability using APM tooling, logging aggregation, distributed tracing, health checks, dashboards, and operational monitoring.
• Own engineering governance through DORA metrics, FinOps visibility, Apptio Cloudability integration, catalog governance, and platform health reporting.
• Design, develop, and operationalize AI agents for platform automation, workflow orchestration, autonomous task execution, and intelligent decision-making.
• Implement GenAI capabilities including RAG, knowledge bases, embeddings, vector storage, retrieval optimization, and client-context-aware agent contextualization.
• Establish AI quality frameworks covering ground-truth validation, hallucination detection, evaluation metrics, validation pipelines, and QA for AI-generated outputs.
• Develop MCP server/client integrations and connect external services, tools, data sources, and third-party platforms through protocol-based integrations.
• Design and optimize AI-driven engineering workflows, identify automation opportunities, implement workflow orchestration patterns, and measure productivity improvements.

Transition platform operations, catalog governance, AI workflows, and BAU support to client ownership while maintaining stakeholder satisfaction and delivery excellence.

[JE1]As we did for Gen AI /RAG , Platform customization role can be generic covering both Build & Run , this will be useful for the new Neuro ITOPS paltform . The candidate should be able to work Platform adoption across platform . Suganya please check with Amit on should we expand on this or keep it focused for Flowsource