AI Gateway Engineer - Jersey City, Tampa & Dallas

Job Role: AI Gateway Engineer

Locations: Jersey City NJ, Dallas TX & Tampa FL

Work mode: Hybrid

Experience: 8 to 12 Years

Employment: W2

We are seeking an experienced Senior AI Gateway Engineer to lead the architecture, engineering, security, and operational management of our enterprise AI Gateway platform with a primary focus on Kong AI Gateway. 

This role will serve as the technical authority responsible for enabling secure, resilient, compliant, and scalable consumption of Large Language Models (LLMs), AI Agents, Retrieval Augmented Generation (RAG) services, Model Context Protocol (MCP) services, and Agent-to-Agent (A2A) communications across the enterprise. 

The successful candidate will combine expertise in Kong AI Gateway, cloud architecture, AI security, identity and access management (IAM), resiliency engineering, and enterprise governance to deliver a highly available AI platform that meets the demands of a regulated financial services environment. 

Key Responsibilities 

AI Gateway Architecture & Engineering 

• Design, implement, and evolve enterprise AI Gateway solutions using Kong AI Gateway and Kong Enterprise. 
• Develop standardized onboarding patterns for applications, AI agents, and business services consuming AI. 
• Engineer reusable integration patterns for OpenAI, Azure OpenAI, AWS Bedrock, Anthropic, Snowflake Cortex, and internal and external AI services. 
• Implement intelligent model routing, failover, traffic shaping, and provider abstraction. 
• Develop custom Kong plugins and integrations supporting AI-specific governance and security requirements. 
• Define scalable control plane and data plane deployment architectures across hybrid and multi-cloud environments. 

Identity & Access Management for AI 

• Architect and implement enterprise-grade identity controls for AI platforms. 
• Integrate Kong AI Gateway with enterprise identity providers and IAM platforms. 
• Implement OAuth 2.0, OpenID Connect (OIDC), JWT, mutual TLS (mTLS), RBAC, ABAC, non-human identities, workload identities, and agent identities. 
• Establish fine-grained authorization controls at the model, agent, tool, prompt, and data source levels. 
• Design identity propagation patterns across AI workflows and MCP services. 
• Partner with security and compliance teams to establish AI governance and Zero Trust controls. 

AI Security, Governance & Risk Management 

• Implement AI security guardrails and policy enforcement mechanisms. 
• Design controls for prompt injection protection, data loss prevention (DLP), PII detection and redaction, content safety enforcement, prompt and response filtering, and model access governance. 
• Establish policy-as-code practices to manage AI controls at scale. 
• Define logging, monitoring, and audit controls supporting regulatory and compliance requirements. 
• Collaborate with Risk, Compliance, Legal, Data Protection, and AI Governance teams. 

Resiliency, Reliability & Operational Excellence 

• Design highly available and resilient AI platform architectures. 
• Establish enterprise resiliency requirements including multi-region deployment strategies, provider failover, cross-cloud recovery patterns, active-active architectures, disaster recovery, and business continuity controls. 
• Implement rate limiting, circuit breakers, load balancing, traffic throttling, semantic caching, and capacity management. 
• Define and manage Service Level Objectives (SLOs), Service Level Indicators (SLIs), error budgets, Recovery Time Objectives (RTOs), and Recovery Point Objectives (RPOs). 
• Conduct architecture reviews, resilience testing, and failure scenario exercises. 

Cloud & Data Platform Integration 

• Design AI access patterns across Microsoft Azure, Amazon Web Services (AWS), and Snowflake. 
• Integrate AI Gateway services with Azure OpenAI, AWS Bedrock, Snowflake Cortex, vector databases, data protection platforms, and enterprise observability tooling. 
• Ensure secure connectivity and standardized governance across multi-cloud environments. 

Observability & Platform Operations 

• Build enterprise observability capabilities for AI workloads. 
• Implement monitoring, metrics, tracing, and audit logging. 
• Analyze token consumption, latency, model utilization, cost optimization opportunities, and security events. 
• Create operational dashboards for engineering, security, risk, and executive stakeholders. 
• Support incident response and platform troubleshooting efforts. 

Required Qualifications 

Education 

• Bachelor's degree in Computer Science, Cyber Security, Information Technology, Engineering, or related discipline. 
• Master's degree preferred. 

Experience 

• 8 years of experience designing and operating enterprise API, application, or cloud platforms. 
• 5 years of experience in cloud architecture and security. 
• 3 years working with AI/ML platform technologies or enterprise AI deployments. 
• Hands-on experience implementing and managing Kong Gateway and Kong Enterprise solutions. 
• Experience within regulated industries such as financial services, banking, insurance, or capital markets strongly preferred. 

Preferred Qualifications

• Kong Certified Professional certification. 
• AWS Solutions Architect certification. 
• Microsoft Azure Solutions Architect certification. 
• CISSP, CCSP, or equivalent security certification. 
• Experience implementing AI security controls and governance frameworks. 
• Familiarity with NIST AI RMF, NIST 800-53, NYDFS 500, PCI DSS, SOC 2, and other financial services regulatory requirements. 
• Experience with DSPM, DLP, and enterprise data protection controls. 

Key Success Metrics 

• Successful deployment and adoption of enterprise AI Gateway services. 
• Reduction of direct AI provider integrations through centralized governance. 
• Achievement of enterprise resiliency and availability targets. 
• Compliance with security, privacy, and regulatory requirements. 
• Improved AI observability, auditability, and cost management. 
• Successful implementation of AI identity and authorization controls. 
• Reduction in AI-related security risks and policy violations.