Senior Data AI Engineer

• Design, develop, and maintain scalable, reliable, and cost-efficient data pipelines and data platforms on GCP (BigQuery, Dataflow, Cloud Storage, Pub/Sub, Composer, Dataproc, etc.)


• Architect and optimize large-scale data warehouses and analytical solutions using BigQuery, focusing on performance, cost optimization, and data quality.


• Build and operationalize end-to-end AI/ML solutions – from data preparation and feature engineering to model training, evaluation, deployment, and monitoring.


• Collaborate with data scientists, product teams, and business stakeholders to translate business requirements into robust data and AI solutions.


• Implement best practices for data governance, data quality, security, and compliance across the data platform.


• Develop CI/CD pipelines for data and ML workflows (using tools like Cloud Build, Vertex AI Pipelines, Terraform, etc.).


• Monitor, troubleshoot, and continuously improve data pipelines and AI model performance in production.


• Mentor junior engineers and contribute to technical decision-making and architecture reviews.


• Stay updated with the latest GCP services, AI/ML advancements, and industry best practices, and proactively recommend improvements.

Required Skills

• 5–8 years of overall experience in Data Engineering


• Strong hands-on experience with GCP Data Platform and BigQuery


• 2–3 years of practical AI/ML experience (must have)


• Proficiency in Python and SQL.


• Experience with ETL/ELT pipelines, data modeling, and large-scale data processing


• Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn) and MLOps practices


• Strong problem-solving and system design skills.

Nice to Have Skills

• Experience with Vertex AI, AutoML, or other GCP AI/ML services


• Knowledge of Apache Spark, Airflow/Composer, Kafka/Pub/Sub


• Experience with Infrastructure as Code (Terraform / Deployment Manager)


• Familiarity with containerization (Docker, Kubernetes/GKE)


• Exposure to real-time streaming pipelines and event-driven architectures


• Experience in building RAG systems, LLM applications, or Generative AI solutions


• Previous experience in mentoring or leading small technical teams


• Certifications: Google Cloud Professional Data Engineer / Machine Learning Engineer

Educational Requirements

1. Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, Artificial Intelligence, or a related field.


2. Equivalent practical experience will also be considered.