Skills - AI Platforms, API Gateways, IAM, OAuth/OIDC, Enterprise Security, AI Governance, Kubernetes, and Cloud
Position Summary
We are seeking an experienced AI Gateway Engineer to design, implement, secure, and operate enterprise AI Gateway platforms that enable governed access to Large Language Models (LLMs), AI agents, foundation models, and AI services across hybrid and cloud environments.
The ideal candidate will have strong expertise in AI Platforms, API Gateways, IAM, OAuth/OIDC, Enterprise Security, AI Governance, Kubernetes, and Cloud Architecture, ensuring secure, scalable, and compliant AI consumption across the organization. The role will focus on AI traffic management, model access control, agent security, policy enforcement, observability, and responsible AI controls.
Key Responsibilities
AI Gateway Platform Engineering
- Design and implement enterprise AI Gateway platforms for GenAI, Agentic AI, and LLM workloads.
- Build and manage centralized ingress and egress gateways for AI applications, agents, MCP servers, and external AI services.
- Develop AI traffic routing, model orchestration, load balancing, failover, and request governance capabilities.
- Implement model abstraction layers supporting multiple AI providers including OpenAI, Azure OpenAI, Anthropic, Google Gemini, AWS Bedrock, and private models.
- Design multi-tenant AI platform architectures for enterprise consumption.
- Establish AI service onboarding standards and platform governance processes.
Identity & Access Management (IAM)
- Design AI-specific authentication and authorization frameworks.
- Implement OAuth2, OpenID Connect (OIDC), SAML, JWT, API keys, and service-account-based access controls.
- Configure RBAC and ABAC policies for AI applications and agents.
- Integrate AI platforms with:
- Microsoft Entra ID (Azure AD)
- Okta
- Ping Identity
- CyberArk
- AWS IAM
- Google Cloud IAM
- Design least-privilege access models for agents, tools, and AI services.
- Implement agent identity and machine-to-machine authentication mechanisms.
- Manage secret handling, token lifecycle, certificate rotation, and privileged access controls.
AI Security & Governance
- Implement enterprise AI Guardrails and Responsible AI controls.
- Configure protections for:
- Prompt Injection
- Jailbreak Attacks
- Malicious URL Detection
- Data Leakage Prevention
- Sensitive Data Masking
- PII Detection
- Content Moderation
- Design AI security policies using Policy-as-Code and Governance-as-Code methodologies.
- Establish model access governance, agent authorization, and inference authorization mechanisms.
- Support compliance frameworks including:
- NIST AI RMF
- NIST CSF
- ISO 27001
- SOC2
- GDPR
- HIPAA
- Conduct AI threat modeling and risk assessments.
Cloud & Platform Engineering
- Build and manage AI platform infrastructure on:
- Azure
- AWS
- Google Cloud Platform
- Deploy and manage Kubernetes-based AI environments.
- Develop Infrastructure as Code using:
- Terraform
- CloudFormation
- Helm
- Automate AI platform provisioning and governance controls.
- Implement CI/CD pipelines for AI platform deployment and policy validation.
- Support highly available and scalable AI runtime environments.
Observability & Monitoring
- Implement AI platform observability frameworks.
- Monitor:
- Model utilization
- Latency
- Cost
- Token Consumption
- Security Violations
- Agent Activities
- Build dashboards using:
- Grafana
- Prometheus
- Splunk
- Datadog
- Azure Monitor
- CloudWatch
- Develop audit and compliance reporting mechanisms.
- Establish AI operational metrics and SLOs.
DevSecOps & Automation
- Integrate security controls into AI delivery pipelines.
- Implement:
- SAST
- DAST
- SCA
- Container Security
- IaC Security Validation
- Automate AI policy enforcement and compliance checks.
- Support software supply chain security and AI BOM/SBOM validation.
- Build reusable platform automation frameworks and reference architectures.
Required Qualifications
Education
- Bachelor's or Master's degree in:
- Computer Science
- Information Technology
- Engineering
- Cybersecurity
- Related discipline
Experience
- 7+ years in Platform Engineering, Cloud Engineering, Security Engineering, or IAM.
- 3+ years in AI/GenAI platform implementation.
- Experience deploying enterprise AI solutions in production environments.
- Strong exposure to secure software development and cloud-native architectures.
Technical Skills
AI Technologies
- Large Language Models (LLMs)
- RAG Architectures
- AI Agents
- MCP Frameworks
- Prompt Engineering
- Vector Databases
- AI Gateway Platforms
- AI Guardrails
- Model Governance
IAM & Security
- OAuth2
- OIDC
- SAML
- JWT
- RBAC
- ABAC
- Zero Trust Architecture
- PAM Solutions
- Secrets Management
Cloud Platforms
- Azure
- AWS
- Google Cloud Platform
Cloud IAM Services
- Microsoft Entra ID
- AWS IAM
- Google Cloud IAM
- Okta
- PingFederate
- CyberArk
DevOps & Platform
- Kubernetes
- Docker
- Terraform
- Helm
- GitHub Actions
- Azure DevOps
- Jenkins
Programming
- Python
- Go
- Java
- PowerShell
- Shell Scripting
Preferred Certifications
AI & Cloud
- Microsoft Certified: Azure AI Engineer Associate
- Google Professional Cloud AI Engineer
- AWS Machine Learning Specialty
- Microsoft AI Engineer
IAM & Security
- CISSP
- CCSP
- Certified Identity & Access Manager (CIAM)
- CyberArk Defender/Sentry
Platform & Cloud
- Certified Kubernetes Administrator (CKA)
- Terraform Associate
- AWS Solutions Architect Professional
- Azure Solutions Architect Expert
Key Competencies
- Enterprise Architecture
- AI Governance
- Platform Engineering
- Security Design
- Identity Architecture
- Stakeholder Management
- Problem Solving
- Automation Mindset
- Risk Assessment
- Technical Leadership
Target Profile
This role is ideal for professionals currently working as:
- AI Platform Engineer
- GenAI Platform Engineer
- Cloud Platform Engineer
- IAM Engineer
- Identity Architect
- API Gateway Engineer
- Security Platform Engineer
- AI Security Engineer
- DevSecOps Engineer
- Kubernetes Platform Engineer
