GCP Data & AI Engineer, AS

Overview

Senior individual contributor role (AVP level) leading architecture and hands-on delivery of complex data and generative AI solutions on GCP, owning solutions from design through production deployment.

What you'll do

• Design, build, and operationalize AI applications, including production-grade RAG pipelines on GCP.
• Leverage the Gemini Enterprise Agent Platform to train, fine-tune, and deploy machine learning and generative AI models.
• Design and implement complex agentic workflows to automate and optimize business processes.
• Design, develop, integrate, secure, version, test, deploy, consume, and operate mission-critical REST APIs using Python, with containerised deployment on Cloud Run.
• Design, develop, and maintain scalable batch and streaming data pipelines using Python, SQL, Cloud Composer, and Pub/Sub.
• Develop and optimize complex SQL queries in BigQuery for large-scale data analysis, extraction, and transformation.
• Automate data quality and ETL testing procedures using Python and SQL.
• Develop and deploy all cloud infrastructure as code using Terraform.
• Implement and manage CI/CD pipelines for data and AI applications, including automated testing, security checks, controlled environment promotion, and reliable production deployment.
• Own security and governance controls for data and AI solutions, including IAM, least-privilege access, encryption, secrets management, audit logging, data protection, and compliance with enterprise standards.
• Serve as a key escalation point for complex L3 production issues, providing expert troubleshooting and resolution.

What you'll need

• 6-10 years of IT experience as a hands-on engineer, including responsibility for designing, building, deploying, and supporting large-scale data systems in production.
• Deep proficiency in Python and advanced SQL, including complex query optimization and data modelling.
• Extensive hands-on experience building solutions on GCP.
• Mastery of BigQuery, Cloud Composer (or Apache Airflow), and Cloud Run in production environments.
• Proficient in defining infrastructure as code using Terraform.
• Designing and managing robust CI/CD pipelines using Cloud Build and Artifact Registry, including automated testing, vulnerability scanning, and environment-based release strategies.
• Deep, practical experience in system design: strong understanding of modern data patterns, distributed systems, and architectural best practices.
• Hands-on experience designing and developing conversational AI solutions and chatbots using Dialogflow CX (Conversational Agents) or CX Agent Studio.
• Extensive hands-on experience building enterprise AI solutions using the Gemini Enterprise Agent Platform, including agent configuration, integration, evaluation, security, deployment, and operational monitoring.
• Proven experience building and deploying production-grade RAG systems, with deep understanding of LLMs (like Gemini), vector databases, and embedding models.
• Strong practical experience designing and developing agentic AI applications, complex workflows, agentic patterns, and multi-agent systems using the Google Agent Development Kit (ADK).
• Demonstrable experience operationalizing AI models and hosting applications using containers (Docker).

Nice to have

• Experience with Azure or AWS is valuable.
• Knowledge of the A2A (agent-to-agent) protocol and agent cards is highly desirable.

Details

• Location: Pune, India.
• Flexible scheme and benefits as described in the posting.