Join Google Cloud's Data Cloud team, a leader in the AI revolution, and contribute to the development of cutting-edge data platforms integrated with Gemini Enterprise. As a Forward Deployed Engineer (FDE), you will be instrumental in transforming innovative data solutions into production-ready realities for our customers. This role offers a unique opportunity to work directly within client environments, accelerating the adoption of Google Cloud's advanced data and machine learning capabilities.

Leverage your expertise in programming languages and the data cloud ecosystem to build robust data foundations for AI agents. You will play a key role in training and evaluating machine learning models on customer data, providing critical feedback to shape Google Cloud's future product roadmap. Be part of a collaborative culture that provides direct access to DeepMind's engineering and research talent.

Build and deploy production-ready data and machine learning workloads, ensuring performance and reliability using PySpark, SQL, and Python on BigQuery, Managed Spark, Spanner, and AlloyDB.

Develop comprehensive data foundations for agentic solutions, including lakehouse architectures, metadata management, and secure access controls.

Train, evaluate, and deploy machine learning and agentic components to drive tangible business improvements for clients, implementing robust monitoring systems.

Present functional proofs of concept using customer data, ensuring sensitive information is de-identified appropriately.

Refactor and migrate high-cost analytics, Spark, and operational database workloads to Google Cloud, demonstrating measurable cost and performance benefits.

Possess a Bachelor's degree in Engineering, Computer Science, or a related field, or demonstrate equivalent practical experience.

Bring at least 2 years of experience in software engineering, data engineering, solution architecture, machine learning, or technical consulting, with strong coding skills in Python and SQL.

Have hands-on experience with data cloud ecosystem products and technologies such as Apache Spark, PySpark, BigQuery, Spanner, Data Catalog, and AlloyDB.

Demonstrate experience in architecting and building production-grade data cloud solutions, including cloud-based data systems and structured/unstructured pipelines on platforms like GCP.

Proven ability to train, evaluate, and deploy machine learning models in production environments is essential. Experience with multi-agent systems and LLM applications is highly desirable.