Back
Specialized ML/LLM and cloud stack reduces mass applicants despite recognizable employer brand.
Role requires ML/LLM, Databricks and GCP skills, moderately transferable but with clear technical domain bias.
Explicit 8+ years, cloud-native, Databricks, GCP, LLM and artifact-management requirements set strict filters.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead design and development of scalable AI/ML pipelines and applications utilizing Google Cloud services including Databricks, AlloyDB, CloudRun, and Google Cloud API Gateway.
Own technical design decisions and provide mentorship to junior engineers, driving innovation and architectural excellence across AI/ML projects.
Implement DevOps practices including artifact management with JFrog Artifactory and automated CI/CD pipelines ensuring efficient deployment and version control.
Minimum 8 years of software development experience with at least 3 years in cloud-native architectures.
Advanced Python programming skills with production experience in ML/AI systems.
Hands-on experience in Google Cloud Platform components: AlloyDB, CloudRun, API Gateway.
Proven expertise with Databricks, LLM integration/application development, containerization (Docker/Kubernetes), and artifact management tools like JFrog Artifactory.
Experienced senior developer comfortable bridging AI/ML engineering and DevOps in a cloud-native, microservices environment.
Strong leadership capability demonstrated through mentoring and leading technical design and architecture decisions.
Practitioner familiar with full AI/ML system lifecycles integrating data engineering, model training, and modern DevOps pipelines in large-scale enterprise projects.