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United States
Remote only
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sr. Systems Engineer – AI Gateway Platform based in the United States.
We are seeking an experienced Systems Engineer to own, scale, and secure an enterprise AI Gateway platform that enables reliable connectivity between AI workloads and critical business services. In this role, you will build and maintain the infrastructure connecting AI agents, Model Context Protocol (MCP) servers, internal microservices, cloud-hosted foundation models, and enterprise SaaS applications. You will help establish secure, high-performance gateway capabilities with robust identity management, traffic controls, and AI-specific security protections. Working closely with product, engineering, enterprise AI, and security teams, you will turn complex requirements into scalable platform solutions. You will also drive automation, observability, and operational improvements to ensure the platform remains resilient as adoption grows. This fully remote opportunity offers the chance to shape enterprise AI infrastructure and support the responsible expansion of AI-powered experiences.
Deploy, configure, operate, and maintain the enterprise Kong AI Gateway platform, ensuring high availability, low latency, and reliable performance under high-throughput AI traffic.
Configure secure routing for Model Context Protocol (MCP) servers, tools, and agents across internal microservices and external SaaS platforms.
Simplify onboarding for internal applications by provisioning standard gateway routes, credentials, and access policies for upstream large language model (LLM) and MCP services.
Partner with engineering and product teams to translate technical requirements into iterative, value-driven delivery plans within an Agile framework.
Identify platform bottlenecks, streamline integration workflows, and continuously improve gateway operations.
Design and implement enterprise Identity and Access Management (IAM) controls for AI workloads, including OAuth 2.0, Okta integrations, non-human workload identities, and secure identity propagation across MCP tool calls.
Manage SSL/TLS certificate lifecycles, mutual TLS (mTLS) configurations, and automated certificate renewal and rotation using tools such as cert-manager.
Implement AI security guardrails, policy enforcement, and Data Loss Prevention (DLP) controls at the gateway boundary, including prompt injection protection, automated personally identifiable information (PII) and secret redaction, and prompt/response filtering.
Develop intelligent traffic management capabilities, including traffic shaping, load balancing, circuit breakers, provider failover, and semantic caching to improve resilience, reduce latency, and optimize token costs.
Build and maintain declarative infrastructure configurations using YAML and Helm, alongside reusable GitLab CI/CD pipelines that support GitOps-driven deployments.
Implement platform observability using OpenTelemetry for distributed tracing and performance metrics, and route structured audit logs into Splunk.
Define and track platform success metrics, including usage, latency, reliability, and security control effectiveness.
Leverage existing Kong plugins and develop custom plugins using Go or Lua when specialized routing, context injection, or governance requirements arise.
At least 8 years of professional engineering experience, with exposure to full-stack application development and enterprise architecture.
Proven experience engineering and supporting enterprise-scale platforms with high-volume traffic and complex integrations across multiple systems.
Strong understanding of API gateway architecture, high-throughput traffic management, and secure-by-design operating models.
Proficiency in modern programming languages used in backend or full-stack development, such as Python, JavaScript, TypeScript, or Go.
Experience working with AI systems and interfaces, such as Claude, ChatGPT, or comparable technologies.
Hands-on experience deploying and configuring containerized applications and services in Kubernetes environments.
Experience building CI/CD automation pipelines and reusable templates using GitLab CI/CD or equivalent tools, as well as declarative infrastructure configurations with YAML and Helm.
Strong technical understanding of RESTful APIs, JSON, microservices architectures, and distributed systems design.
Solid background in IAM, including authentication and authorization protocols such as OAuth 2.0, OpenID Connect (OIDC), JSON Web Tokens (JWT), and mTLS, as well as identity platforms such as Okta.
Experience with enterprise observability, monitoring, and alerting using tools such as OpenTelemetry and Splunk.
Knowledge of service reliability planning and disaster recovery practices.
Experience working in Agile engineering environments, with the ability to work autonomously, take initiative, and deliver in fast-paced settings.
Familiarity with enterprise service delivery and collaboration platforms such as Jira, ServiceNow, and Slack.
Ability to assess technical systems from both service-provider and service-consumer perspectives.
Strong collaboration skills, with the ability to work effectively with security, risk, compliance, infrastructure, and product stakeholders on sensitive, highly governed API and AI initiatives.
Demonstrated experience using AI technologies to improve workflows, optimize processes, and accelerate execution.
Experience delivering enterprise platforms that enable user-driven experiences.
Bonus qualifications:
Master's degree in Computer Science, Artificial Intelligence, or a related technical discipline.
Experience with Go or Lua for low-latency gateway extensions, and TypeScript/Node.js or Python for MCP services and integration tooling.
Experience designing and implementing volume, feasibility, and automated testing frameworks to validate scalability, high-throughput performance, and production readiness.
Experience managing security vulnerabilities and remediation across client and backend applications, including automated package, dependency, and container image remediation.
Annual base salary: $125,000–$180,000, depending on relevant experience, skills, certifications, job level, and geographic location.
Eligibility for bonus compensation and equity grants.
Comprehensive health insurance coverage.
401(k) retirement plan.
Paid time off and competitive holiday allowances.
Paid parental and adoption leave.
Physical and mental wellness programs.
Professional development opportunities for employees at all levels.
Employee networks, geographic community groups, and volunteer opportunities.
Flexible remote work arrangement within the United States.
