Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience with software development in Java, Kotlin or equivalent programming languages.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
• 3 years of experience building and developing large-scale infrastructure, distributed systems or networks, or experience with compute technologies, storage, or hardware architecture.

Preferred qualifications:

• Experience designing and building RESTful or gRPC APIs for internal and external consumers.
• Knowledge of model serving frameworks and platforms (e.g., TensorFlow Serving, TorchServe, Triton Inference Server, Vertex AI Prediction).
• Familiarity with Google Cloud Platform (GCP) services relevant to AI/ML infra, such as Vertex AI, Google Kubernetes Engine (GKE), Cloud Storage, BigQuery, etc.
• Excellent ownership and problem solving skills, and familiarity with system design.

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.

Software Engineers that work for Cloud AI's team in Poland specialize in designing, building, and maintaining the scalable and reliable backend infrastructure that powers GenAI systems. Their primary focus is on the "middleware" layer, sitting between the user-facing APIs and the underlying ML models and data stores.

Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

Responsibilities

• Architect and implement backend libraries, services and systems to support AI/ML workflows, including agentic frameworks and protocols, model serving platforms, feature stores, data pipelines, and API gateways.
• Focus on optimizing the performance, latency, throughput, and resource utilization (e.g., GPU/TPU, memory) of the middleware components.
• Ensure the infrastructure can handle varying loads, scale efficiently, and maintain high availability and fault tolerance.
• Work closely with ML engineers, data scientists, and application developers to integrate models and data sources into the serving infrastructure.
• Automate deployment, testing, and operational tasks related to the AI/ML infrastructure (MLOps practices).