Senior AI Engineer

Quick Facts

• Role: Senior AI Engineer

• Work mode: Hybrid (1 day per week in Warsaw office)

• Project budget: up to 190 PLN net + VAT/hour (depending on experience and scope)

Description

Work on a bank-focused engagement to transform software engineering and enable organization-wide adoption of generative AI (GenAI) across the software lifecycle. You will analyze existing engineering process metrics, define and implement end-to-end measurement, integrate data from development tools, build KPI dashboards, and identify bottlenecks and optimization opportunities. You will also measure GenAI adoption and its impact on productivity, software quality, and ROI—translating technical results into business-relevant KPIs.

Responsibilities

• Analyze current process efficiency metrics and select new indicators aligned with organizational goals

• Implement end-to-end metrics: data source definition, automated data acquisition, visualization, and reporting

• Collect, integrate, and analyze data from Jira, GitLab, GitHub, and Gerrit using API and SQL

• Define, calculate, and monitor DORA metrics, Lead Time for Changes, Cycle Time, and Flow Metrics

• Design, build, and develop KPI dashboards and executive reports in Power BI

• Analyze results to find constraints and improvement areas in software engineering processes

• Translate technical findings into business outcomes (time-to-market, delay cost, operational risk, customer experience impact)

• Measure GenAI effects, including adoption level, productivity and quality impact, and ROI of licenses

• Support initiatives integrating LLMs with business processes/tools

• Recommend process improvements and monitor the impact of implemented changes

• Document metric definitions, measurement methodologies, and reports in Confluence

• Present analysis results, conclusions, and recommendations to technical teams and management

Requirements

• Minimum 5 years of experience in data analytics, Platform Engineering, DevOps, or Agile/IT processes

• Very good knowledge of Jira (JQL, workflows, status history, dashboards)

• Very good knowledge of Confluence

• Practical knowledge of GitLab (CI/CD, pipelines, deployments, Merge Requests)

• Practical knowledge of GitHub (repos, Pull Requests, GitHub Actions, API)

• Practical knowledge of Gerrit (code review processes, patchsets, code review time metrics)

• Very good knowledge of SQL (JOIN, aggregations, CTE, window functions)

• Experience with Power BI (data modeling, Power Query, DAX, dashboards)

• Knowledge of LLMs (capabilities/limitations, hallucination risk, data security, cost risks)

• Practical prompt engineering knowledge and basic RAG architecture

• Experience integrating LLMs with applications/business processes via API

• Practical knowledge of GitHub Copilot and ability to measure adoption and productivity impact

• Experience using Microsoft Copilot in daily analytical work

• Knowledge of AI/knowledge management tools such as NotebookLM or Google Vertex AI

• Very good knowledge of DORA metrics and their components

• Understanding of Lead Time for Changes vs Cycle Time and ability to define/calculate them

• Ability to select and implement metrics for organizational goals

• Ability to interpret KPI indicators (medians/percentiles, trend analysis, and combining speed with stability/quality)

• Ability to translate technical metrics into business context (Lead Time, Change Failure Rate, Rework Rate)

• Ability to convert analysis into actionable recommendations and measurable KPI goals

• Ability to assess data quality, completeness, and consistency before analysis and decisions

Benefits

• Ability to deliver across diverse projects in Software, Embedded, Data, and Cloud Services

• Participation in additional pre-sales and expert initiatives

• Access to medical care, a sports card, and a cafeteria platform per collaboration rules