Minimum qualifications:
- Bachelor's degree or equivalent practical experience.
- 2 years of experience in designing, building, and deploying NLP models and generative AI agents.
- Experience implementing DevOps and MLOps pipelines.
- Experience in building generative AI solutions in a customer-facing role.
- Experience in ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, and debugging) and coding in Python.
Preferred qualifications:
- Master’s degree or PhD in AI, Computer Science, or a related technical field.
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s ADK) and complex patterns like ReAct, self-reflection, and hierarchical delegation.
- Knowledge of "LLM-native" metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
- Proven ability to implement secure agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication.
- Ability to communicate in French, Spanish, Italian or other European languages.
About the job
Google Cloud’s mission is to make every business successful through AI by combining cutting-edge technology, infrastructure, and talent. AI/ML software engineers in Cloud bridge the gap between pioneering models and a massive product vehicle reaching billions. Our talent density and AI-powered tools drive rapid development, rooted in a culture of empowerment and a bias to action. In this role, you aren’t just building technology; you’re shaping the frontier of enterprise and driving the evolution of advanced models.Leading with the future, the AI GTM team are trusted AI advisors, scaling across Google to deliver transformative customer experiences that shape the next generation of their business. Partnering to provide deep AI expertise, thought leadership, and industry best practices, we revolutionize customer outcomes.
As an AI Forward Deployed Engineer (FDE) at Google Cloud, you will be an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. Unlike traditional advisory roles, you will function as a builder-consultant, moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment.
This role is designed for high-agency engineers with a founder’s mindset. You will address blocker to production including solving the integration complexities, data readiness issues, and state-management issues that prevent AI from reaching enterprise-grade maturity. You will serve a dual purpose: providing white glove deployment of complex AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.
It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours. Individual pay is determined by factors including job-related skills, experience, and relevant education or training.Poland: zł280000 - zł287000 (PLN) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Serve as the lead developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable Return on Investment (ROI).
- Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
- Build high-performance evaluation (Eval) pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency.
- Identify repeatable field patterns and technical "friction points" in Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.
- Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
