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Beqom

Total compensation management software for enterprises

Senior Agentic Backend Engineer

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeKraków, PolandNo salary listedHybrid

About the Job

Requirements

  • 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 recent production output should be AI-generated under the candidate's direction.
  • Hands-on mastery of AI coding agents, with Claude Code strongly preferred, including parallel agent sessions, custom skills and sub-agents, and structuring codebases and context so agents work reliably.
  • Ability to turn ambiguous business needs into precise, agent-executable Product Requirement Prompts and spec files.
  • Strong verification engineering experience, including behavioural and scenario-based testing, holdout evaluation, quality gates, and validation frameworks.
  • Strong architecture judgement to assess whether agent-proposed solutions meet performance, scalability, and reliability requirements and to challenge designs that will not hold up under real workloads.
  • Experience building agent infrastructure, including MCP servers and tool layers, sandboxed execution, and CI/CD adapted for agent-driven workflows.
  • Deep .NET/C# and SQL reading fluency to audit generated code when quality gates flag it, including schema design, query plans, streaming versus buffering, and memory behaviour.
  • Excellent written communication for specifications, scenario definitions, and design documents that humans and agents can act on.

Responsibilities

  • Deliver features across multi-tenant data platform services—including data ingestion, schema management, effective dating, snapshots, calculated fields, and exports—exclusively through AI agents working from specifications.
  • Author structured, testable Product Requirement Prompts and specification files as the primary engineering artefacts, and maintain the versioned specification library governing the codebase.
  • Run multiple agents in parallel across workstreams; evaluate outcomes against specifications; and improve agent first-pass success rate, defect escape rate, and the share of changes merged without human review.
  • Build and maintain the agentic pipeline, including an agent harness, a blueprint engine interleaving deterministic steps such as lint, build, and git with AI reasoning, an MCP tool layer exposing internal APIs, a warm sandbox pool, and retrieval over the codebase, tickets, and historical pull requests.
  • Own pipeline economics through observability over token usage, task latency, and cost per run, as well as model-tier routing and cost-per-outcome gating.
  • Embed governance into the pipeline through machine-generated audit trails, named ownership per feature, and data-classification gates that keep production secrets and personally identifiable information out of agent context.
  • Engineer verification rather than code through external behavioural scenario suites and holdout sets stored outside the codebase, digital twins of integrated systems, layered AI-review gates, and automated remediation for self-healing services.
  • Set and enforce constraints that keep agent output correct on performance-critical paths, including bulk ingestion throughput, large export streaming, memory behaviour, and complex SQL, validated through scenario and load gates rather than line-by-line reading.
  • Diagnose and resolve production data issues through agents, using deep functional understanding of owned services to steer remediation in collaboration with Support and Engineering.
  • Document the specifications, scenarios, and pipeline decisions governing the platform as artefacts humans and agents can act on, and help other engineers make the same shift.

Desired Qualifications

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

About the Company

Beqom

Total compensation management software for enterprises

Beqom provides Total Compensation Management software for large enterprises through a Software-as-a-Service (SaaS) platform. The cloud-based system handles all pay elements—salaries, bonuses, incentives, benefits—and includes modules for sales incentives, performance, quotas, and territory management, with customization to fit each organization. It differentiates itself by offering an enterprise-grade, highly adaptable solution that works across industries and large client deployments, helping clients streamline compensation workflows and reduce salary-review times. Beqom’s goal is to improve efficiency in compensation planning, increase employee satisfaction, and drive better business outcomes by optimizing how pay is planned, awarded, and reviewed.

Company size
201-500
Company stage
Growth Equity (Venture Capital)
Total funding
$359.6M
Headquarters
Nyon, Switzerland
Founded
2009

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