出海雷达

高级代理后端工程师

Senior Agentic Backend Engineer

AI开发工程限定地区(需当地身份)日间重叠仅 1 小时,需熬夜配合
公司beqom
薪资未公开
工作地点United Kingdom
地域资格限定地区(需当地身份)
时区要求日间重叠仅 1 小时,需熬夜配合
用工类型Full Time
发布时间17 天前
数据来源Himalayas
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注意地域限制:该职位明确限定在 United Kingdom 招聘。如果你是位于中国大陆的求职者,通常需要当地工作身份才能投递,或需与雇主确认是否接受独立合同(Contractor)形式合作。
作息提示:日间重叠仅 1 小时,需熬夜配合。

加入 beqom — 技术与影响力交汇的地方
beqom 是一家高增长的 B2B SaaS 公司,为薪酬公平性与透明度、薪酬和绩效管理提供行业领先的工具。
被世界上一些最受尊敬的公司所信赖,beqom 让 HR 和业务领导者能够应对全球合规要求,并做出更明智的薪酬决策,以吸引、保留和激励顶尖人才。
beqom 在瑞士成立,服务客户遍布全球,我们的强大且企业就绪的产品由 beqom 薪酬智能驱动。
职位概述
我们正在寻找一名高级代理后端工程师加入我们的数据平台核心团队,该团队负责 beqom 数据架构的核心服务——多租户数据摄入、模式管理、快照、计算字段以及支撑产品中分析和报告的数据合同。
在这个职位上,你不会编写代码。AI 代理会。你将负责管理这些服务的规范,将这些规范转化为生产软件的代理流程,以及验证输出正确的机制——明确目标是实现完全嵌入 AI 的开发:没有人工编写的代码,没有人工逐行审查,外部行为场景而非传统测试套件作为质量门禁。
你将与数据平台和分析工程师、支持团队以及产品利益相关者紧密合作——将他们的需求转化为代理可执行的规范。规范是我们记录的系统;代理完成类型化工作。
你将从事的工作
代理交付

  • 通过 AI 代理,根据你的规范,跨多租户数据平台服务交付功能——数据摄入、模式管理、生效日期、快照、计算字段和导出。
  • 编写结构化、可测试的产品需求提示(PRPs)和规范文件作为主要工程产物,并维护管理代码库的版本化规范库。
  • 并行运行多个代理处理不同工作流;根据规范评估结果;稳步提升代理首次通过率、缺陷逃逸率以及无需人工审查的更改占比。

流程工程

  • 构建并维护代理流程:代理框架、蓝图引擎将确定性步骤(lint、build、git)与 AI 推理交织在一起,MCP 工具层暴露内部 API,温暖沙盒池,以及对代码库、工单和历史记录的检索。
查看英文原文

Join beqom — where tech meets impact
beqom is a high-growth B2B SaaS company that provides industry-leading tools for pay equity and transparency, compensation, and performance management.
Trusted by some of the world’s most respected companies, beqom enables HR and business leaders to navigate global compliance and make smarter pay decisions that attract, retain, and motivate top talent.
Founded in Switzerland and serving clients worldwide, our powerful, enterprise-ready products are fuelled by beqom pay intelligence.
Role Overview
We are looking for a Senior Agentic Backend Engineer to join our Data Platform Core team, which owns the services at the heart of beqom’s data architecture — multi-tenant data ingestion, schema management, snapshots, calculated fields, and the data contracts powering analytics and reporting across the product.
In this role you will not write the code. AI agents will. You will own the specifications that govern these services, the agentic pipeline that turns those specifications into production software, and the verification machinery that proves the output is correct — with the explicit goal of reaching fully AI-embedded development: no human-written code, no human line-by-line review, and external behavioural scenarios, not traditional test suites, as the quality gate.
You will collaborate closely with Data Platform and Analytics engineers, Support teams, and Product stakeholders — translating their needs into agent-executable specifications. Specs are our system of record; agents do the typing.
What you'll be doing
Agentic Delivery

  • Deliver features across multi-tenant data platform services — data ingestion, schema management, effective dating, snapshots, calculated fields, and exports — exclusively through AI agents working from your specifications.
  • Author structured, testable Product Requirement Prompts (PRPs) and spec files as the primary engineering artefact, and maintain the versioned spec library that governs the codebase.
  • Run multiple agents in parallel across workstreams; evaluate outcomes against specs; progress agent first-pass success rate, defect escape rate, and the share of changes merged without human review steadily upward.

Pipeline Engineering

  • Build and maintain the agentic pipeline: agent harness, blueprint engine interleaving deterministic steps (lint, build, git) with AI reasoning, MCP tool layer exposing internal APIs, warm sandbox pool, and retrieval over the codebase, tickets, and historical PRs.
  • Own the economics of the pipeline: observability over token usage, task latency, and cost per run; model-tier routing and cost-per-outcome gating.
  • Embed governance into the pipeline — machine-generated audit trails, named ownership per feature, data-classification gates keeping production secrets and PII out of agent context.

Verification Engineering

  • Engineer verification, not code: external behavioural scenario suites and holdout sets stored outside the codebase (agents never see the evaluation criteria), digital twins of the systems we integrate with, layered AI-reviews-AI gates, and automated remediation for self-healing services.
  • Set and enforce the constraints that keep agent output correct on performance-critical paths: bulk ingestion throughput, large export streaming, memory behaviour, and complex SQL — validated by scenario and load gates rather than line-by-line reading.

Production Ownership & Collaboration

  • Diagnose and resolve production data issues through agents — with deep enough functional understanding of every service you own to steer remediation quickly, in collaboration with Support and Engineering.
  • Document the specs, scenarios, and pipeline decisions that govern the platform — artefacts both humans and agents can act on — and help other engineers make the same shift.

Skills & Experience
What you'll bring

  • Demonstrated senior-level experience building production backend services — including .NET/C# and PostgreSQL systems — with a demonstrable shift from writing code to directing agents: the majority of your recent production output should be AI-generated under your direction.
  • Hands-on mastery of AI coding agents — Claude Code strongly preferred — including parallel agent sessions, custom skills and sub-agents, and structuring codebases and context so agents work reliably.
  • Excellence in specification writing — turning ambiguous business needs into precise, agent-executable PRPs and spec files. In this role, spec quality is the single most load-bearing skill.
  • Strong verification engineering: behavioural and scenario-based testing, holdout evaluation, quality gates, and validation frameworks — the ability to trust and judge output you did not write.
  • Strong architecture judgement: assessing whether an agent-proposed solution meets performance, scalability, and reliability requirements, and challenging designs that will not hold up under real workloads.
  • Experience building agent infrastructure: MCP servers and tool layers, sandboxed execution, and CI/CD adapted for agent-driven workflows.
  • Deep enough .NET/C# and SQL reading fluency to audit generated code when quality gates flag it — schema design, query plans, streaming vs. buffering, memory behaviour.
  • Excellent written communication: specs, scenario definitions, and design docs that both humans and agents can act on.

Bonus points if you have

  • Experience building digital twins or service virtualisations of external systems for agent evaluation environments.
  • Experience with LLM evaluation at scale: eval suites, red-teaming AI-generated code, benchmarking agent workflows.
  • Multi-tenant SaaS architecture experience, CDC/event-driven ingestion (Debezium, Kafka, RabbitMQ/MassTransit), or lakehouse exposure (Microsoft Fabric).
  • Azure services, Docker, Kubernetes/Helm.
  • Experience with legal, security, or compliance frameworks for AI-authored code.
  • Familiarity with compensation, HR, or pay transparency domains.

Why join us?

  • Your career, your design. Unleash your ambition in our dynamic, autonomous environment.
  • Drive meaningful change. Build a fairer future for every employee by joining a market leader that is improving the world of work.
  • Belong to something bigger. Collaborate with a passionate, diverse and talented team around the globe.

Originally posted on Himalayas

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