远程工作雷达

数据工程师 – 人工智能价值与DX平台

Data Engineer – AI Value & DX Platform

AI开发工程未标注地域
公司qualysoft
薪资未公开
工作地点Budapest
地域资格未标注地域
时区要求无特别要求
用工类型未标注
发布时间未知
数据来源Lever
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成立于1999年的维也纳,Qualysoft集团是一家与厂商无关的IT咨询和服务公司,通过创新的IT解决方案成功为国际客户提供支持,以提升其竞争力和经济效益。

其业务重点包括金融服务提供商、电信公司、汽车工业和能源服务提供商。在6家子公司中,超过400名员工共同努力,为客户提供最先进的解决方案。

我们正在寻找新的同事加入Qualysoft团队,参与多样化的项目,提供持续学习的机会。我们的共同目标是提供诚实、发展和稳定的基础,同时了解最新的技术。我们期待您申请以下职位!
为什么我们认为您会喜欢在这里工作:

在这里,您被视为一个完整的人,我们的大门永远向您敞开。
我们践行Qualysoft团队精神,坚持透明度!

新鲜的思路和新想法受到欢迎,因为停滞不前在Qualysoft是一个陌生的词。

项目概述

该项目专注于DX平台(getdx.com)的实施作为概念验证(PoC),重点在于客观衡量AI辅助软件开发工具所产生的价值。

像GitHub Copilot、编码代理和AI辅助代码审查解决方案等AI驱动的开发工具正被越来越多地采用。然而,它们的实际业务价值目前很难可靠地衡量。

PoC的目标是评估AI的采用情况、使用强度、开发时间节省以及对质量和交付周期的影响是否可以被可靠地衡量、集成到现有系统中,并用于支持有依据的ROI、投资和扩展决策。

主要职责

  • 支持试点团队的选择和AI价值衡量模型的定义。
  • 定义涵盖AI采用、使用强度、时间节省、交付周期、质量影响和ROI的衡量和成功标准。
  • 设置和配置DX平台,包括SSO/身份集成和权限模型。
  • 与现有的GitLab、CI/CD、工单和身份/SSO系统集成DX。
  • 集成正在使用的AI工具的使用和遥测数据,包括GitHub Copilot使用数据/Copilot Metrics API。
  • 在DX平台上配置团队、层级和数据模型。
  • 设置并验证AI工具和DX之间的身份和团队映射。
查看英文原文

Founded in 1999 in Vienna, the Qualysoft Group is a manufacturer-independent IT consulting and services company, which successfully provides support for its international customers with the aim of boosting their competitiveness and economic efficiency through innovative IT solutions.

Its focus is on financial services providers, telecommunications companies, the automotive industry and energy service providers. Over 400 employees in 6 subsidiaries work together to ensure state of the art solutions for our clients.

We are looking for new colleagues in Qualysoft teams for diverse projects providing continuous learning opportunities. Our common goal is to provide honesty, development and a stable background while getting to know the latest technologies. We are waiting for your application for the position below!
Why we think you will love working here:

With us you count as a person, our doors are always open.
We live the Qualysoft Team Spirit and stand for transparency!

Fresh wind and new ideas are welcome, because standstill is a foreign word at Qualysoft.

Project Overview

The project focuses on the implementation of the DX platform (getdx.com) as a Proof of Concept (PoC), with a strong focus on objectively measuring the value generated by AI-assisted software development tools.

AI-powered development tools such as GitHub Copilot, coding agents, and AI-assisted code review solutions are increasingly being adopted. However, their actual business value is currently difficult to measure reliably.

The objective of the PoC is to assess whether AI adoption, usage intensity, development time savings, and the impact on quality and lead times can be reliably measured, integrated into existing systems, and used to support well-founded ROI, investment, and scaling decisions.

Key Responsibilities

  • Support the selection of pilot teams and the definition of an AI value measurement model.
  • Define measurement and success criteria covering AI adoption, usage intensity, time savings, lead time, quality impact, and ROI.
  • Set up and configure the DX platform, including SSO/identity integration and the permission model.
  • Integrate DX with existing GitLab, CI/CD, ticketing, and identity/SSO systems.
  • Integrate usage and telemetry data from the AI tools in use, including GitHub Copilot usage data / Copilot Metrics API.
  • Configure teams, hierarchies, and data models within the DX platform.
  • Set up and validate identity and team mapping across AI tools and delivery systems.
  • Link AI-related metrics with relevant cost data.
  • Set up the DX AI Measurement Framework surveys and system-side data collection.
  • Establish baseline measurements before and after the expansion of AI usage.
  • Analyze the impact of AI on quality-related metrics such as change failure rate, review effort, rework, and security findings, ensuring that AI value is not assessed solely in terms of speed.
  • Ensure data quality, consistency, and completeness, including error analysis and correction.
  • Implement technical requirements related to anonymization, aggregation, and data minimization.
  • Ensure a clear separation between AI usage measurement and individual performance measurement.
  • Develop dashboards, reports, and exports to support results analysis and ROI assessment.
  • Identify the most effective AI use cases and key barriers to adoption based on the evaluation results.
  • Support the definition of enablement measures and the future scaling and license strategy.
  • Prepare technical documentation and estimate the effort and resources required for future scaling.
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