Senior Gemini AI Engineer with GCP

Job Title :: Senior Gemini AI Engineer with GCP
Location:: Dallas, TX - Hybrid
Job type: Contract 
Duration: 6+months
Open for C2C

You will bridge the gap between cutting-edge generative AI research and enterprise-grade production software, creating multi-agent orchestration workflows that securely integrate with enterprise data streams.

Key Responsibilities

• Agentic System Architecture: Design and deploy production-grade autonomous AI agents and multi-agent systems using the Google Agent Development Kit (ADK), Vertex AI Agent Builder , and advanced orchestration concepts (e.g., ReAct, reflection, and memory management).
• Gemini Implementation: Optimize, fine-tune, and deploy Gemini Enterprise models via Vertex AI , 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 BigQuery 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 GCP 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 .
• GCP Expertise: Deep, hands-on experience utilizing Google Cloud Platform (GCP), 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.
• DevOps & LLMOps: Experience setting up CI/CD workflows, containerized architectures (Docker, Kubernetes), and utilizing LLMOps observability tools (e.g., LangSmith, MLflow, Phoenix).