Lead GCP Engineer - AI Platforms (Remote)

2d2 days ago

Concentrix

Indianapolis, US · Full-time · $122,001 – $191,715

About this role

As Lead GCP Engineer you serve as the resident expert and engineering heart of AI and agentic capabilities within the Google Cloud ecosystem. You own the technical standard for building scalable agentic systems and bridge the gap between AI models and production-ready applications.

Day-to-day work centers on building production-grade microservices and APIs that orchestrate agent workflows using Gemini Enterprise, Vertex AI Agent Builder, and the Agent Development Kit. You implement prompt templates, system instructions, and few-shot examples while tuning agentic behavior through iterative testing.

You collaborate with specialists across the consultancy to share best practices while delivering strategic consulting and intelligence-fueled experiences. The environment surrounds you with market-leading technology and deep industry expertise that accelerates transformation at scale.

Join a global team recognized year after year with awards for World's Best Workplaces, Best Companies for Career Growth, and Best Company Culture. Shape new game-changing careers that power the world's best brands today and into the future.

Requirements

Expertise in Google Cloud Platform services including Vertex AI, Gemini models, Cloud Run, GKE, and Cloud Functions.
Hands-on experience building and scaling agentic AI systems and production microservices.
Proficiency implementing prompt engineering, function calling, grounding, and agent orchestration patterns.
Strong background in data integration using BigQuery, Vertex AI Search, and vector databases.
Experience establishing CI/CD pipelines and MLOps practices for LLM applications.

Responsibilities

Build and maintain production-grade microservices and APIs that orchestrate agent workflows using Gemini Enterprise, Vertex AI Agent Builder, and the Agent Development Kit.
Implement and optimize complex prompt templates, system instructions, and few-shot examples for Gemini models while tuning agentic behavior through iterative testing.
Lead the transition of AI prototypes to scalable production environments using Cloud Run, GKE, Vertex AI Agent Engine, and Cloud Functions.
Implement connectors and glue code required for agents to interact with enterprise data via Vertex AI Search, BigQuery, and specialized vector databases.
Establish automated deployment pipelines with Cloud Build and MLOps-aligned engineering practices for LLM-based systems.