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Strong global brand but senior, specialized AI role reduces applicant density.
Specialized ML, GCP, Databricks, and LLM experience makes cross-industry moves moderately transferable.
Explicit 8+ years and many mandatory cloud, Databricks, LLM, and DevOps technology requirements.
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Own the design and implementation of scalable AI/ML pipelines and cloud-native applications on Google Cloud, using technologies like Databricks, AlloyDB, CloudRun, and Google Cloud API Gateway.
Lead technical design reviews, architectural decisions, and mentor junior engineers to drive innovation and technical excellence across the platform.
Develop and optimize LLM applications leveraging Google's Gemini API and establish CI/CD pipelines with artifact management and deployment automation.
8+ years of software development experience with at least 3+ years in cloud-native architectures.
Advanced Python skills with production experience in ML/AI systems.
Hands-on experience with Google Cloud Platform (AlloyDB, CloudRun, API Gateway) and Databricks for data and ML engineering.
Experience with artifact repositories (JFrog Artifactory or equivalent), microservices, containerization (Docker/Kubernetes), and modern DevOps practices.
Experienced in bridging machine learning development and DevOps in cloud-native environments, especially on Google Cloud Platform.
Proven leadership capabilities in mentoring and guiding engineering teams through technical challenges and architectural decisions.
Strong background integrating or developing applications with large language models (LLMs) and modern ML frameworks, capable of handling complex ambiguous problems.