AI Engineering Manager (m/f/n)

InPost Group; Kraków; On-site; PLN 320,000 - 520,000; Full time

Job summary

InPost Group in Kraków leads the Data & AI organization to build AI-native solutions across the group. The role guides AI engineering strategy, sets development standards, and partners with product, data, and technology stakeholders to shape future AI products.

You will oversee a team of 10–15 engineers, drive collaboration, and influence architecture and processes while ensuring inclusive practices and vendor collaborations.

Qualifications

• Leadership of a multidisciplinary engineering team (~10–15) in AI.
• Own AI Engineering strategy and contribute to ML-powered products.
• Partner with product managers and stakeholders to define requirements and delivery plans.
• Establish standards for AI-native products, including CI/CD with evals, test sets, and search subsystems.

Responsibilities

• Lead AI engineering team and drive growth, autonomy and collaboration.
• Define requirements and priorities with product managers and stakeholders.
• Set standards and processes for AI-native product development.
• Select and implement tools including MCP Servers, A2A, CI/CD pipelines and workflows.
• Guide new AI-native workflows involving streaming and long-running tasks.
• Improve system architecture, design, and processes to enhance product functionality.
• Contribute to hiring, onboarding and professional development.
• Collaborate with vendors and tech partners on partnerships and innovation.

Skills

Team leadership

AI strategy

Cross-functional collaboration

CI/CD

Tool evaluation

Tools

MCP Servers

A2A communication

CI/CD pipelines

Job description

Help us build the next generation of AI-powered productsJoin the Data & AI organization and take ownership of engineering teams building AI-native solutions for the entire Group. This opportunity combines technology leadership with a meaningful influence on products, engineering practices, team development, and the organization’s direction in artificial intelligence.You will help shape the AI Engineering strategy, establish new software development standards, and partner closely with product, data, and technology stakeholders. Together, we will explore practical applications of emerging technologies, including multi-agent systems, AI agents, and tools supporting the evaluation of AI models and applications.

Your impact

• Lead a multidisciplinary engineering team of approximately 10–15 people working with AI technologies, supporting their growth, autonomy, and effective collaboration.


• Own the AI Engineering strategy and contribute to the development of products powered by machine learning and AI-native technologies.


• Partner with product managers and key stakeholders to define requirements, priorities, delivery plans, new features, and product enhancements.


• Establish standards and processes for building AI-native products, including CI/CD with evals, test sets, and search subsystems.


• Select and implement best-in-class open-source and commercial tools, including MCP Servers, A2A communication, CI/CD pipelines, workflows, and agent definitions.


• Guide the development of new AI-native workflows involving streaming, long-running tasks, and procedural UX, balancing state-of-the-art technology with pragmatism.


• Drive improvements in system architecture, application design, and engineering processes to enhance product functionality and customer experience.


• Contribute to hiring, onboarding, and the professional development of team members while supporting a diverse and inclusive workplace.


• Collaborate with strategic vendors and technology partners on partnerships, service agreements, and external innovation.

Technologies and areas you may work with

Python, LangSmith, AI evaluation stacks, OpenAI, Gemini, Databricks, multi-agent systems, MCP Servers, A2A communication, CI/CD, test sets, search subsystems, streaming, and long-running AI tasks.

What makes this role distinctive

The opportunity to shape the AI Engineering strategy and define standards for AI-native product development. Meaningful influence on a developing team, its engineering culture, and its ways of working. A Group-wide scope rather than responsibility limited to one local business area. Close collaboration with product, data, technology, and key business stakeholders. The opportunity to combine emerging technologies with a pragmatic approach to scaling products and engineering processes. Budget responsibility and influence over relationships with vendors and technology partners.