Sr Staff Software Engineer, Applied AI (f/m/d)

The Senior Staff Software Engineer, Applied AI defines and drives the AI engineering strategy across multiple teams and product domains, setting the architectural direction for our GenAI capabilities and ensuring their long-term scalability, reliability, and impact. This role operates at the intersection of deep technical expertise and organizational leadership: shaping how we build with AI as an organization, not just contributing to that build.

You will act as a key technical partner to engineering leadership, product teams, and cross-functional stakeholders, aligning people across silos, resolving architectural misalignments, and creating the conditions for teams to move fast and confidently with AI. You bring a systems-level mindset, lead through influence and leave things meaningfully better than you found them — for the technology, and for the people around you.

This is an on-site role in Kraków.

What you will do:

• Define and evolve the AI engineering architecture across multiple teams and product domains establishing long-term technical direction for GenAI capabilities and making high-impact decisions with real product and business implications.

• Lead cross-team AI initiatives from conception through delivery, reducing friction, duplication, and misalignment across organizational boundaries.

• Set org-wide standards for how teams build with GenAI — including RAG architectures, LLM integration patterns, agentic frameworks, and vector search strategies.

• Act as a key technical partner to engineering managers, product leadership, and cross-functional stakeholders in shaping AI strategy, roadmap, and investment decisions.

• Drive architecture reviews and system design for AI initiatives with significant technical and business implications and help others do the same.

• Identify and proactively address long-term risks in AI systems before they become blockers anticipating what others haven't seen yet.

• Evaluate, select, and champion AI tooling (LangGraph, LangChain, Cloud AI services, vector stores) at an organizational level, not just for one team, but as reusable, scalable foundations.

• Ensure model observability, reliability, quality, and responsible operation across the AI engineering landscape.

• Mentor and sponsor Staff and Senior Engineers helping them grow in technical depth, system thinking, and engineering maturity; contributing to leveling and hiring conversations.

• Stay ahead of the AI landscape and translate emerging technologies into concrete, org-level opportunities and strategies.

Who you are:

• Proven org-level technical impact in the GenAI or AI/ML space with track record of not just building great systems, but shaping how organizations build with AI.

• Deep expertise in Generative AI, LLMs, RAG architectures, prompt engineering, and production-grade AI system design. You've seen what good looks like and you've built it.

• Demonstrated ability to lead without formal authority - aligning engineers, managers, and stakeholders around a shared technical vision across organizational silos.

• Strong architectural thinking at scale: you design for long-term flexibility, organizational complexity, and cross-team reuse not just the immediate problem in front of you.

• Experience delivering end-to-end AI solutions from prototype to production, with cross-team coordination and organizational impact.

• Strong software engineering fundamentals — clean code, testing, CI/CD, API design — and the credibility and judgment to raise the bar on these across teams, not just within your own work.

• Ability to drive clarity in ambiguous spaces — synthesizing complex, messy problems into clear architectural direction and actionable plans that others can execute on.

• Experience working with cloud services in production AI environments.

• Strong communication skills — you can articulate technical strategy to both engineers and non-technical leadership with equal clarity and conviction.

It would be a plus if you also possess:

• Experience defining and leading org-wide AI platform initiatives, architecture guilds, or internal AI engineering communities of practice.

• A track record of introducing standards, patterns, or frameworks that were adopted broadly across an engineering organization — not just within a single team.

• Experience scaling AI capabilities in fast-moving product organizations with all the ambiguity and tradeoffs that come with it.

• Hands-on mastery with Python, TypeScript and modern frameworks (e.g., LangGraph, LangChani, Vercel AI SDK) 

• Background in developer tooling, internal platform engineering and productivit focused AI products.

• Familiarity with responsible AI principles and practical experience implementing observability, evaluation pipelines, and safety guardrails in production systems.