GenAI Principal Architect: Enterprise AI & MLOps Lead

Genuine-Parts-Company

Kraków

Hybrid

PLN 180,000 - 260,000

Full time

14 days+

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Job summary

Genuine Parts Company in Kraków seeks a Principal Engineer in the Generative AI Innovation Hub to set technical direction for enterprise GenAI capabilities while remaining hands-on in architecture and development. You will lead rapid experimentation and partner with product, data, platform and security teams to move high-value use cases from prototype to production.

This senior individual-contributor role influences multiple teams, mentors engineers, and defines reusable architectures,

Qualifications

10+ years of software engineering experience with significant technical leadership.
Recent practical experience designing, delivering, and operating AI/GenAI solutions in production.
Advanced Python skills and strong software architecture, APIs, testing, and maintainable practices.
Hands-on understanding of GenAI patterns including retrieval, agentic workflows, and evaluation.
Experience with cloud platforms, distributed systems, containers, CI/CD and production observability.
Ability to incorporate security, privacy and risk considerations into designs.
Proven ability to influence architecture across teams without formal authority.
Excellent communication to explain tradeoffs to technical and business audiences.

Responsibilities

Set and evolve the technical strategy, reference architectures, and engineering standards for enterprise generative AI solutions.
Architect and build prototypes and production services using Python, APIs, cloud platforms, and modern software engineering practices.
Lead the evaluation of foundation models, retrieval‑augmented generation, agentic workflows, and model or vendor options, including build‑versus‑buy decisions.
Establish LLMOps and MLOps practices for evaluation, testing, versioning, deployment, observability, incident response, and continuous improvement.
Embed responsible AI, data protection, security, and human‑oversight requirements throughout solution design and delivery.
Partner with business and product leaders to frame use cases, define measurable outcomes, and prioritize experiments based on value, feasibility, and risk.
Integrate generative AI capabilities with existing applications, enterprise data, and business processes.
Lead architecture and design reviews, mentor engineers across teams, and raise the bar for engineering quality.
Monitor relevant advances in AI and machine learning and recommend adoption when evidence supports meaningful business value.
Promote disciplined experimentation with explicit learning goals, evaluation criteria, and decision gates.