AWS AI Platform SME & IAM

Job Description

Job Overview: A Platform Engineer is responsible for setting up, configuring, governing, and supporting SaaS-based AI code-generation tools for enterprise engineering teams. This role focuses on enabling secure and scalable adoption of AI-assisted development platforms by configuring tool settings, integrating identity and access management, implementing role-based access controls, and documenting configurations, standards, and operating procedures. Responsibilities: SaaS AI Tool Setup: Configure and administer SaaS tools for AI-based code generation, including tenant settings, workspace structures, usage policies, and integration options. Identity and Access Management: Set up and maintain authentication flows, SSO integrations, Active Directory or enterprise directory mappings, and user provisioning processes. Role-Based Access Controls: Define, implement, and review role-based access controls for developers, administrators, reviewers, and support teams to ensure least-privilege access. Tool Integration: Integrate AI code-generation tools with source control, IDEs, CI/CD pipelines, ticketing systems, and developer portals where appropriate. Governance and Security: Apply enterprise security standards for authentication, authorization, data protection, audit logging, usage monitoring, and policy enforcement. Configuration Documentation: Create and maintain clear documentation covering tool configuration, access models, SSO setup, directory group mappings, operational procedures, and change history. User Onboarding and Support: Support onboarding, access requests, troubleshooting, training, and adoption of AI code-generation tools across development teams. Continuous Improvement: Monitor tool usage, collect feedback, review access periodically, and recommend improvements to configuration, governance, and developer experience. Technical Skills: SaaS Administration: Experience configuring enterprise SaaS platforms, managing tenants or workspaces, applying policies, and supporting users at scale. AI Code-Generation Tools: Familiarity with tools such as GitHub Copilot, AWS Bedrock, Claude Code, Cursor, or similar AI-assisted development platforms. Identity and Authentication: Strong understanding of SSO, SAML, OAuth, OIDC, MFA, authentication flows, enterprise identity providers, and Active Directory or Azure Active Directory concepts. Role-Based Access Control: Ability to design and maintain RBAC models, permission sets, administrator roles, group-based access, and least-privilege access patterns. User Provisioning: Knowledge of directory group mappings, lifecycle management, access request workflows, deprovisioning, and periodic access reviews. Source Control and Developer Tooling: Strong understanding of Git, repository management, IDE integrations, pull request workflows, and developer productivity tools. CI/CD and SDLC Tools: Experience with tools such as Jenkins, Bamboo, GitLab, Bitbucket, Jira, Confluence, Nexus, Zephyr, and Ansible. Security and Compliance: Understanding of audit logging, secrets handling, data protection, secure configuration, SCA, SAST, DAST, and policy enforcement. Documentation: Ability to produce clear configuration guides, operational runbooks, access-control matrices, troubleshooting guides, and change records. Scripting and Automation: Skills in scripting languages such as Python, Bash, PowerShell, Groovy, or Go to automate administrative and reporting tasks. AI Skills: AI Code-Generation Platform Administration: Configure SaaS-based AI coding assistants for enterprise use, including workspace settings, model or feature controls, policy options, and developer enablement settings. Secure AI Tool Adoption: Apply governance practices for AI-assisted coding, including access controls, auditability, acceptable-use policies, secure configuration, and usage monitoring. Identity-Aware AI Enablement: Integrate AI coding platforms with SSO, Active Directory, enterprise identity providers, and role-based access models to support secure user onboarding and administration. AI-Assisted Coding and Scripting: Use AI coding assistants to accelerate Python, Bash, PowerShell, Groovy, and pipeline script development while validating generated code for correctness, security, and maintainability. AI Tool Configuration Documentation: Document configuration decisions, access models, SSO and directory mappings, policy settings, exception handling, and operational procedures for AI tooling. Prompt Engineering for Engineering Workflows: Create clear prompts for troubleshooting, documentation, onboarding, operational runbooks, and developer support scenarios. AI-Enhanced SDLC Optimization: Use AI to improve code review preparation, test generation, pipeline troubleshooting, documentation quality, and developer productivity while respecting enterprise guardrails. Soft Skills: Collaboration: Excellent teamwork and communication skills to work effectively with cross-functional teams. Problem-Solving: Strong analytical and troubleshooting abilities to resolve issues quickly. Adaptability: Ability to adapt to new technologies and methodologies in a fast-paced environment. Attention to Detail: Precision in managing configurations and deployments to avoid errors. Continuous Learning: Commitment to staying updated with the latest trends and tools in platform engineering. TPBN1_UKTJ