Experience Level:

6+ years of experience in data engineering or data platform development, with at least 2+ years in the Google Cloud environment.

Role Overview:

We are looking for an experienced Google Cloud Data Engineer with robust hands-on expertise in Vertex AI and the Google Cloud AI ecosystem. The ideal candidate will design and implement scalable data pipelines, enable ML workflows, and collaborate with AI/ML engineers and data scientists to operationalize data-driven solutions.

Key Responsibilities:

- Design and build scalable data pipelines using Google Cloud services such as BigQuery, Dataflow, Dataproc, Pub/Sub, and Cloud Storage.
- Implement end-to-end ML workflows on Vertex AI covering data preparation, feature engineering, model training, tuning, deployment, and monitoring.
- Integrate AI/ML models into production systems using MLOps best practices.
- Work with BigQuery ML, Vertex AI Workbench, and AI APIs (Vision, NLP, Speech, Translation, Generative AI) to build intelligent, data-driven solutions.
- Build and maintain data orchestration pipelines using Cloud Composer / Airflow and automate retraining using Cloud Build or GitHub Actions.
- Ensure data pipelines meet requirements for performance, security, governance, and lineage.
- Monitor model performance and drift, ensuring timely retraining and optimization.
- Partner with cross-functional teams to design data architectures that support both analytics and AI use cases.
- Evaluate emerging tools and techniques in the Google AI stack (e.g., Gemini, Vertex AI Agent Builder, RAG, LangChain integrations) to enhance the platform s intelligence layer.





Required Skills & Experience:

- Strong expertise in Google Cloud Platform (GCP) services including BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Storage, and Cloud Composer.
- Hands-on experience with Vertex AI for model training, hyperparameter tuning, deployment, and monitoring.
- Knowledge of Google AI/ML APIs (Vision, Natural Language, Speech, Translation, Generative AI models).
- Proficiency in SQL, Python, and Apache Beam/Spark for data engineering.
- Understanding of feature engineering, model versioning, and data lineage within ML pipelines.
- Familiarity with CI/CD pipelines for ML (MLOps best practices).
- Experience integrating structured and unstructured data sources.
- Knowledge of data governance, IAM, and security in GCP.

Nice-to-Have:

- Google Cloud Certified: Professional Data Engineer or Machine Learning Engineer.
- Experience with RAG (Retrieval-Augmented Generation) solutions and LLM fine-tuning on Vertex AI.
- Familiarity with Looker, Data Studio, or other visualization tools.
- Experience in cross-functional collaboration with AI research and product engineering teams.

Soft Skills

- Excellent problem-solving and analytical abilities.
- Strong communication and stakeholder management skills.
- Passion for leveraging AI and cloud technologies to deliver measurable business impact.

Find Your Next Role

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Google Cloud Data Engineer (Bengaluru)
🏢 Vation Digital
📍 Bengaluru