Response Informatics
AWS AI Platform Engineer
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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.
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.
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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.
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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.
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