Position:

Senior Gemini AI Engineer Location:

Dallas, TX Duration:

Long term

Key Responsibilities Agentic System Architecture:

Design and deploy production-grade autonomous AI agents and multi-agent systems using the Google Agent Development Kit (ADK), , and advanced orchestration concepts (e.g., ReAct, reflection, and memory management).

Gemini Implementation:

Optimize, fine-tune, and deploy Gemini Enterprise models via , integrating advanced capabilities like tool calling, multimodal data processing, and contextual grounding.

Enterprise Integration:

Build secure, real-time integrations between AI agents and enterprise platforms utilizing Model Context Protocol (MCP) toolbox, REST/SOAP APIs, and database structures like and Cloud Spanner.

RAG Pipeline Engineering:

Develop and maintain secure Retrieval-Augmented Generation (RAG) pipelines using managed tools like Vertex AI Vector Search, AlloyDB, or BigQuery vector engines.

Cloud Infrastructure & Pipelines:

Architect scalable streaming and batch pipelines on Google Cloud Platform using Dataflow , and host workloads via cloud-native runtimes like Cloud Run or Google Kubernetes Engine (GKE).

AI Governance & Security:

Implement enterprise-grade security protocols, including IAM roles, VPC Service Controls, PII masking, prompt injection prevention, and Model Armor guardrails.

Required Qualifications & Skills Experience:

10+ years of professional software engineering experience, with at least 2+years of dedicated focus on Agentic AI systems, LLM orchestration, and generative AI applications .

Google Cloud Platform Expertise:

Deep, hands-on experience utilizing Google Cloud Platform (Google Cloud Platform), specifically Vertex AI, Model Garden, and Vertex AI Pipelines.

Agent Frameworks:

Proven mastery over the Google Agent Development Kit (ADK) and workflow tools. Familiarity with broader open-source orchestration frameworks like LangGraph, CrewAI, AutoGen, or LlamaIndex is highly valued.

Core Languages:

Strong proficiency in Python for machine learning workflows and production API development, alongside advanced SQL skills for BigQuery.