Back
Tier-1 employer, metro location, popular ML role, and broad full-stack plus MLOps requirements increase competition.
ML/MLOps specialization raises domain sensitivity, though cloud and full-stack skills are transferable.
Requires specific cloud, Vertex AI, MLOps, and full-stack tech stack, though no explicit years.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design and build cloud-based data pipelines and AI/ML models on GCP, including ingestion, transformation, training, deployment, and monitoring using Vertex AI and MLOps.
Develop and integrate production-grade full-stack applications and APIs with strong UI/UX, including Angular frontends and dashboards.
Implement CI/CD pipelines for automated build, test, and deployment; extract and cleanse data from diverse databases using SQL and other query languages.
Experience with cloud platforms, specifically Google Cloud Platform (GCP) and tools like BigQuery, Snowflake, Vertex AI, Cloud Build.
Proficiency in AI/ML model development, deployment, and monitoring, and MLOps practices.
Strong full-stack development skills including Angular, API integration, and dashboard development.
Work Experience Required: Not explicitly mentioned in the JD.
Technical breadth in cloud-based AI/ML pipeline design and implementation with hands-on GCP knowledge.
Experience in production environment full-stack engineering combining backend pipeline skills with frontend UI/UX.
Comfortable working with automated CI/CD workflows and integrating data engineering with AI model operations.