Senior Engineer - AI Enablement

Description

Minimum compensation: starting from 18 000 PLN. The final offer will be determined based on the candidate's experience and competencies.

Proven AI Impact

• Achieved measurable productivity improvements using AI in development

• Implemented AI-assisted refactoring, test generation, or documentation at scale

• Experience with AI code analysis and automated remediation

• Track record of shipping production systems built with AI assistance

Backend Engineering

• 8+ years of software engineering with Java backend expertise

• Experience modernising production systems at scale

• Strong API design and microservices architecture knowledge

• Understanding of strangler fig patterns, service decomposition, and legacy migration strategies

The Perks

• Home office equipment reimbursement

• Performance-related bonus

• Private medical cover for you and your family/partner (Medicover)

• Multikafeteria system (you can choose a multisport card, vouchers, etc.)

• Life insurance (Generali)

• LinkedIn Learning platform free access

• Share Plans for Employees

• Employee-led LGBTQ+, Women’s, Black, and Parents & Carers networks with an annual budget for organizing events & projects that foster an open, diverse, and inclusive culture

• Career-focused technical and leadership training in-class and online, incl. unlimited access to LinkedIn Learning platform

• Well-being events as well as Employee Assistance Programme

• Summer picnic, New Year party and other social events

• 3 additional days off a year - 1 to celebrate your Birthday and 2 for voluntary work

• App-based parking spots booking system

• Stretching sessions

• Wellbeing weeks

Ship Results Quickly

• Complete service modernisations in fast cycles with monthly milestones

• Use AI to accelerate every phase: analysis, refactoring, testing, documentation

• Hand off modernised services to Platform Services or divisions with clear ownership

• Demonstrate measurable improvements: faster APIs, better performance, higher reliability

How we work

• Lead and Inspire: Drives trust, alignment, and enthusiasm

• Think Big: Focus on the problems that most impact commercial outcomes

• Champion the client: Understand and prioritise client's needs

• Deliver at pace: Push for fast, sustainable growth;

• Raise the bar: Take ownership, be accountable and share feedback

Demonstrate AI-First Development

• Use AI for all coding tasks: refactoring, test creation, documentation, debugging

• Achieve measurable and significant productivity improvements through AI integration

• Document patterns and share learnings through your work

• Train teams during service handoffs on AI-enabled workflows you've built

• Demonstrate when to use AI vs when human judgment is critical

Modernise Legacy Services Using AI

• Use AI to analyse codebases, understand dependencies, and extract clean APIs

• Work on high-impact legacy services that block divisional delivery speed

• Implement strangler fig patterns and other proven migration approaches

• Deliver modernised services with comprehensive tests, documentation, and multi-instance deployment capabilities

Build AI-Powered Development Infrastructure

• Implement Model Context Protocol (MCP) servers for service discovery, dependency mapping, and architecture compliance

• Create AI-assisted CI/CD pipelines with automated code review, security scanning, and test generation

• Build automation using Claude Code, GitHub Copilot, and LLM APIs

• Develop reusable AI tooling that other engineers can adopt

AI/LLM Implementation (Critical Differentiator)

• Hands-on experience building with Model Context Protocol (MCP)

• Demonstrated use of Claude Code, GitHub Copilot, or similar AI development tools in production work

• Experience implementing AI in CI/CD pipelines (code review, testing, security scanning)

• Built agentic AI solutions or AI-powered automation tools

• Understanding of prompt engineering, model selection, and LLM capabilities/limitations

Culture & Leadership Style – What Matters Most

• Delivery-focused: Ship working modernised services, don't just build tools

• Hands-on: Write code daily, don't just architect or advise

• Pragmatic: Use AI to go faster, not to be clever

• Teacher through doing: Others learn by seeing your PRs and shipped work

• Measurement-driven: Track your AI productivity gains and share data

• Collaborative: Work across divisions and time zones professionally