Sr. Systems Engineer – AI Gateway Platform(Remote)

CrowdStrike•USA CA Remote, CA

4d•$125,000 - $180,000•Remote

About The Position

CrowdStrike is seeking an experienced Systems Engineer for its AI Platform Enablement team to own, scale, and secure the enterprise Kong AI Gateway platform. The role delivers a governed, resilient AI connectivity layer for internal adoption, connecting AI workloads—including Model Context Protocol (MCP) servers, agent skills, and autonomous agents—with enterprise services, AWS Bedrock, and SaaS platforms such as Slack, Jira, Salesforce, and Google. The engineer partners with product managers, Enterprise AI teams, application engineers, and security stakeholders to provide reliable gateway patterns, identity and access controls, and secure, scalable connectivity. The position covers deployment and operation of the gateway, secure routing and application onboarding, AI-focused IAM and security controls, certificate lifecycle management, traffic optimization, and GitOps-driven infrastructure delivery. It also involves observability and audit logging, operational improvement, and use of Kong plugins or custom Go or Lua plugins to support routing, context injection, and governance needs. CrowdStrike describes itself as a global cybersecurity company whose platform processes nearly 3 trillion events per day. The role is remote. The U.S. base salary range is $125,000–$180,000 per year, with eligibility for bonuses and equity grants.

Requirements

At least 8 years of professional engineering experience, with familiarity and experience in full-stack application development and enterprise architecture.
Experience supporting and engineering enterprise-scale platforms with high-volume traffic and complex multi-system integrations.
Understanding of API gateway architecture, high-throughput traffic management, and secure-by-design operations in complex enterprise environments.
Familiarity with full-stack functionality and proficiency in modern backend or full-stack programming languages such as Python, JavaScript/TypeScript, or Go.
Experience with AI systems and interfaces such as Claude, ChatGPT, or similar.
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, and declarative infrastructure configurations such as YAML and Helm.
Technical understanding of REST APIs, JSON, microservices, and distributed systems design.
Strong IAM background, including OAuth 2.0, OIDC, JWT, mTLS, and identity platforms such as Okta.
Experience with enterprise observability, monitoring, and alerting based on platform health metrics, such as OpenTelemetry and Splunk.
Experience with service reliability planning and disaster recovery execution.
Experience in Agile engineering teams; ability to self-start, work autonomously, and deliver value-driven outcomes in a fast-paced environment.
Familiarity with enterprise service delivery and collaboration platforms such as Jira, ServiceNow, and Slack for sprint execution, work tracking, incident workflows, and cross-team communication.
Ability to evaluate technical systems from both service-provider and consumer perspectives.
Ability to partner effectively with security, risk, compliance, infrastructure, and product leaders on sensitive or high-governance API and AI initiatives.
Experience using AI technologies to enhance workflows, optimize processes, and accelerate execution.
Experience delivering enterprise platforms that enable user-driven experiences.

Nice To Haves

Master’s degree in Computer Science, Artificial Intelligence, or a related technical field.
Experience using development and scripting languages to optimize build and runtime performance, such as Go or Lua for gateway extensions and TypeScript/Node.js or Python for MCP services and integration tooling.
Experience designing and executing volume, feasibility, and automated testing frameworks to validate scalability, high-throughput traffic handling, and architectural viability under production-scale loads.
Experience managing security vulnerabilities and remediation across client and backend applications, including automated package, dependency, and container-image remediation to maintain compliance and secure the software supply chain.

Responsibilities

Deploy, configure, and maintain the enterprise Kong AI Gateway platform for high availability, low latency, and high-throughput AI traffic.
Configure secure routing for MCP servers, tools, and agents across internal microservices and external SaaS ecosystems.
Onboard internal consumer applications by provisioning standard gateway routes, credentials, and access policies for upstream LLM and MCP services.
Work with engineering and product personnel to translate technical requirements into iterative execution plans within an Agile framework.
Identify process optimizations, remove platform bottlenecks, and improve gateway operations and integration workflows.
Design and implement AI-focused IAM controls, including OAuth 2.0, Okta integrations, non-human workload identities, and secure identity propagation across MCP tool calls.
Manage SSL/TLS certificate lifecycles, mTLS configurations, and automated certificate renewal and rotation.
Enforce AI security guardrails, gateway policy controls, and DLP, including prompt-injection protection, PII and secret redaction, and prompt and response filtering.
Implement traffic shaping, load balancing, circuit breakers, provider failover, and semantic caching to improve latency, resilience, and token costs.
Build and maintain declarative IaaS configurations using YAML and Helm, and reusable GitLab CI/CD pipelines for GitOps-driven deployments.
Instrument the gateway with OpenTelemetry and route structured audit logs to Splunk; establish platform usage, latency, and control-effectiveness metrics.
Use Kong plugins and develop custom Go or Lua plugins when needed for routing, context injection, or governance.