GCP AI Engineer
Role summary
Seeking a GCP AI Engineer in Irving, TX or Charlotte, NC to design, build, and deploy AI agents and integrate LLMs into core products. Responsibilities include implementing Model Context Protocol (MCP), automating code generation, writing scalable Java/Python code for AI backend infrastructure, and utilizing Google Cloud AI services like Vertex AI and Gemini APIs. The role requires strong proficiency in Java and Python, hands-on experience with Google Cloud AI tools, deep expertise in LLMs (prompt engineering, fine-tuning, RAG), and experience with agentic workflows using frameworks like LangChain. Familiarity with containerization (Docker, Kubernetes) and vector databases is preferred.
HI
Our client is looking for a GCP AI Engineer in Irving, TX / Charlotte, NC. Below is the detailed requirement.
Job positing
Title
: GCP AI Engineer
Location
: Irving, TX / Charlotte, NC
Required Skills:
GCP, LLM, Generative AI, and Java/ Python
Job description:
- Bachelor’s degree in computer science, Information Technology, or a related field
- AI Agent Development: Design, build, and deploy autonomous AI agents capable of reasoning, planning, and executing complex workflows.
- LLM Integration: Integrate cutting-edge Large Language Models (LLMs) into our core products and services to enhance functionality and user experience.
- Model Context Protocol (MCP) Implementation: Utilize the Model Context Protocol (MCP) to securely connect our AI models to various data sources, tools, and development environments.
- Automated Code Generation: Leverage AI and LLMs to build systems that assist in, or fully automate, code generation, testing, and optimization processes.
- System Engineering: Write clean, scalable, and maintainable code in both Java and Python to support AI backend infrastructure.
- Google Ecosystem Integration: Utilize Google ADK (AI Developer Kits) and related Google Cloud AI services (e.g., Vertex AI, Gemini APIs) to deploy robust AI solutions.
- Cross-Functional Collaboration: Work closely with product managers, data scientists, and frontend engineers to translate business requirements into technical AI solutions.
- Programming Languages: Strong proficiency in both Java and Python, with a proven track record of building production-grade software.
- Google AI Tools: Hands-on experience with Google ADK (or equivalent Google Cloud AI/Vertex AI tools).
- LLM Expertise: Deep comfort level and practical experience working with Large Language Models (prompt engineering, fine-tuning, RAG architectures).
- Agentic Workflows: Demonstrable experience in building and orchestrating AI Agents (using frameworks like LangChain, LangGraph, or custom implementations).
- MCP Knowledge: Familiarity and practical experience with the Model Context Protocol (MCP) for standardizing AI interactions with external tools.
- Code Generation: Experience in leveraging AI tools or building pipelines specifically for code generation and software automation.
- Experience with modern robust backend frameworks (e.g., Spring Boot for Java, FastAPI for Python).
- Familiarity with containerization and orchestration (Docker, Kubernetes).
- Experience with vector databases (e.g., Pinecone, Weaviate, Milvus).
