We are building the inline cloud data plane that powers a next-generation AI security platform. This service terminates TLS, inspects and processes enterprise AI traffic in real time, and applies policy, classification, orchestration, auditing, and analytics — at millisecond latencies under production SaaS load across multiple global regions.

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Apply Here

Apply Here

We are looking for a hands-on Principal Engineer to own the architecture, design, and implementation of this data plane end to end.

You have shipped and operated an inline, multi-tenant, customer-traffic data plane at one of the following (or equivalent):

- Cloudflare (edge / Workers / Gateway), Fastly, Akamai
- Zscaler (ZIA / ZPA data plane, ZEN / PoP teams)
- Netskope (NewEdge / SWG / inline steering — not Intune, notification, marketing, or config teams)
- Palo Alto Networks (Prisma Access data plane)
- Cato Networks, Menlo Security, Skyhigh, iBoss, Versa, Aryaka, Fortinet FortiSASE
- Hyperscaler edge / gateway teams: AWS (CloudFront, API Gateway, App Mesh), Azure Front Door / App Gateway, Google Cloud (Cloud CDN, Traffic Director)
- Large-scale API gateway or service mesh platform teams at Stripe, Uber, Netflix, LinkedIn, Datadog, or similar

What You'll Do:

- Architect and build the inline cloud data plane that processes enterprise AI traffic in real time.
- Own the request path end to end: TLS termination, HTTP/2 and HTTP/3 handling, policy evaluation, classification hooks, upstream routing, and response streaming.
- Design multi-tenant, multi-region PoP architecture with strict p50 / p95 / p99 latency budgets and availability SLOs.
- Design and implement request routing, service discovery, resiliency, timeout, retry, circuit-breaking, and backpressure strategies on the hot path.
- Optimize the platform for added latency, throughput, connection density, TLS handshakes/sec, and cost per million requests.
- Drive cost and operational efficiency across the data plane, including capacity planning, autoscaling behavior, infrastructure utilization, and cost per unit of traffic.




- Integrate the data plane with policy enforcement, classification, orchestration, auditing, analytics, and other core platform services.
- Define the control-plane integration model for configuration distribution, policy propagation, service discovery, tenant isolation, and secure rollout of data-plane changes.
- Build distributed tracing, metrics, and end-to-end observability tuned for inline request-path debugging.
- Architect Kubernetes-based deployments, horizontal autoscaling, rolling upgrades, and multi-region disaster recovery for the data plane.
- Drive technical decisions on proxy technology selection (Envoy vs. custom vs. hybrid), extension model (filters, WASM, native), and control-plane integration.
- Mentor engineers and set the engineering bar for inline-service development, incident response, and operational excellence.

Required Qualifications:

- 10+ years of software engineering experience, with at least 5 years building or operating an inline, customer-traffic, low-latency SaaS data plane (proxy, gateway, edge, CDN, SASE/SWG, or equivalent).
- Production experience with a programmable L7 proxy — Envoy, Nginx/OpenResty, HAProxy, Squid, or a comparable in-house proxy — including writing filters, modules, or extensions.
- Deep hands-on expertise with HTTP/1.1, HTTP/2, HTTP/3 (QUIC), gRPC, WebSockets, TLS (termination, MITM/SSL-inspection, SNI, session resumption, cert management at scale), and streaming architectures.
- Proven track record of hitting hard SLOs on a live data plane: peak RPS, p99 added latency, TLS handshakes/sec, concurrent connections — you can quote your numbers.
- Strong expertise in multi-tenant, multi-region service design, including PoP architecture, global load balancing,



and failover.
- Deep understanding of Kubernetes and cloud-native application design as it applies to inline, stateful / long-lived-connection workloads.
- Strong systems-programming skills in Go (preferred), Rust, C++, or Java.
- Experience with observability platforms — OpenTelemetry, Prometheus, Grafana, or Datadog — for high-cardinality, request-path observability.
- Excellent architectural, debugging, and performance-optimization skills under production load.

Preferred Qualifications:

- Experience building or extending Envoy (filters, xDS control plane), API gateways (Kong, Apigee, Tyk), service mesh (Istio, Linkerd, Consul Connect), or edge/CDN platforms.
- Production experience with SWG, SASE, CASB, ZTNA, or forward/reverse proxy products.
- Experience with AI/LLM traffic patterns (streaming responses, long-lived SSE/WebSocket connections, token-level inspection).
- Experience designing systems supporting high-throughput traffic processing, horizontal autoscaling under sudden load, graceful degradation, and per-tenant fault isolation.

Notice: Concentric AI never asks for money nor paid certifications during the interview process; such behavior is a known scam of which we’ve been made aware.

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