Senior / Lead Full Stack Engineer - Backend Edge Detection

Company: QAIL AI (qail.ai)
Team: Founding Engineering Location: Remote (US time zones preferred)
Type: Full-time · Senior/Lead

About QAIL
QAIL is building the intelligence layer for the agentic web. We detect and verify the AI agents and bots hitting our customers' websites in real time, qualify legitimate buying intent, and expose MCP endpoints that let autonomous agents discover and transact with businesses. Our detection runs at the AWS edge on CloudFront and Lambda@Edge, processes high-volume traffic with sub-second latency, and closes the loop back to ad platforms through Click ID attribution.
We're a small founding team moving fast on a category that's forming right now. You'll have real ownership over the systems at the core of the product.

The Role
We're looking for a senior/lead full stack engineer with deep backend strength to own — and set technical direction for — our edge detection and signal pipeline: the part of QAIL that sees every request, fingerprints it, and decides in milliseconds whether it's a human, a known AI agent, or something trying to look like one.
This is a high-throughput, low-latency problem. You'll be comfortable thinking about request volume, p99 latency, and the difference between what you can compute at the edge versus what belongs in the aggregation layer. As one of our most senior engineers, you'll make the architectural calls, set the standards the team builds against, and mentor as we grow. You'll touch frontend too — the customer dashboard and our embeddable detection script — but the center of gravity is backend and edge infrastructure.

What You'll Do
Build and own detection at the AWS edge (CloudFront + Lambda@Edge): User-Agent and ASN matching against published AI provider ranges, TLS fingerprinting (JA3/JA4), and request-shape signals — all before the page is served.
Design the signal aggregation pipeline that fuses edge signals, server-side behavioral data (timing, path depth, headers), honeypot/pixel hits, and fingerprint consistency into an identity and intent decision.
Engineer the system to handle large traffic volumes reliably and cheaply — latency budgets, backpressure, caching, and graceful degradation under load.
Build and maintain our MCP endpoints and attribution API that close the loop between agent activity and the customer's ad platforms.
Develop the embeddable client script (fingerprint capture, form integration) and contribute to the real-time analytics dashboard customers use to see bot traffic and lead quality.
Set technical direction — own architecture decisions, establish engineering standards, review code, and mentor engineers as the team scales.
Make pragmatic architecture calls appropriate to our stage — ship, measure, iterate; no premature microservices.

Our Stack
Backend: Java (primary) for core services and the signal pipeline; Python a plus. Edge functions (Lambda@Edge / CloudFront Functions) run in JavaScript/Node and Python.
Edge: AWS CloudFront, Lambda@Edge
Detection signals: TLS/JA3/JA4 fingerprinting, ASN/IP reputation, behavioral & timing analysis, browser fingerprinting
Protocols/APIs: MCP (Model Context Protocol) endpoints, REST, Click ID attribution feedback
Frontend: Modern JS/TS, React-based dashboard, vanilla JS/TS embeddable scripts
Infra: AWS-native; managed services over self-hosted where the cost delta is modest

What We're Looking For
7+ years of backend engineering, with a track record building high-traffic, latency-sensitive systems (APIs, pipelines, or real-time services at scale).
Strong proficiency in Java (our primary backend language); Python a plus, plus comfort writing edge functions in JavaScript/Node or Python (Lambda@Edge / CloudFront Functions) and working across the full stack.
Hands-on experience with AWS edge / serverless (CloudFront, Lambda@Edge, Lambda) — or deep CDN/edge-compute experience you can transfer quickly.
Strong grasp of HTTP, TLS, networking, and request lifecycle fundamentals.
Demonstrated technical leadership: owning architecture, setting standards, and raising the bar for engineers around you.
A pragmatic, ownership-driven mindset suited to an early-stage team: you scope, build, and ship end to end.

Bonus Points
Background in bot detection, anti-fraud, ad tech / mar tech, or web security.
Familiarity with fingerprinting techniques (TLS, canvas/WebGL, behavioral) from either the detection or evasion side.
Experience with MCP, agentic systems, or LLM application infrastructure.
Startup experience strongly preferred — you've thrived in an early-stage, high-ambiguity environment before.
Big plus if you've been a startup founder yourself — you know what it takes to build from zero and own outcomes end to end.

What We Offer
A founding-team seat in an emerging category, with influence over product and technical direction.
High autonomy and direct ownership of core product systems.
Remote-first, fast-moving, low-bureaucracy environment.