AI Senior Engineer

Moondigo Sp. z o.o. operates in the AI domain, focusing on the integration and deployment of AI tools within software development processes.

• Minimum 5 years of technical experience (e.g., Software Developer, DevOps Engineer, QA/Test Automation Engineer) with knowledge of full application lifecycle and production teams.
• Experience designing and deploying AI tools in SDLC, such as GitHub Copilot, Claude Code, Cursor, Codex, M365 Copilot, or open-source models.
• Practical skills in creating AI agents, workflows, skills, prompts, and integrating them with repositories, CI/CD, code review, and testing.
• Knowledge of Spec-Driven Development, LLM, RAG, automation, agent architectures, and modern AI-supported software engineering practices.
• Experience in production-level AI solution deployments including configuration, system integration, hardening, monitoring, and iterative development.
• Ability to gather and translate business and technical needs into implementation initiatives and solution architecture, including conducting technical workshops.
• Skill in assessing AI business value, optimizing model costs, and measuring deployment effects.
• Strong communication and consultation skills collaborating with technical teams, managers, and directors.
• Experience in knowledge transfer through pair/mob programming, code review, and collaborative solution design and building.
• Ability to unify AI work standards and adapt Agentic SDLC practices for different roles such as developers, testers, analysts, and DevOps.

Nice to have:

• Experience with LangGraph and MCP.
• Supporting teams in taking ownership of implemented solutions via joint work and technical sessions.
• Familiarity with software production in large financial organizations and related security and compliance requirements.
• Analytical approach, independence, openness to experimentation, and continuous learning.

• Implementing, configuring, and integrating AI tools and solutions within IT development teams.
• Designing and building AI workflows, usage standards, and technical documentation.
• Providing practical engineering support to teams during AI solutions deployment and evolution.
• Facilitating knowledge transfer and unifying AI practices among teams.
• Supporting teams in independently maintaining and evolving deployed AI solutions.