What are the responsibilities and job description for the Google Cloud Platform AI Engineer position at Lorven Technologies, Inc.?
HI
Our client is looking for a Google Cloud Platform AI Engineer in Irving, TX / Charlotte. Below is the detailed requirement.
Job positing
Title: Google Cloud Platform AI Engineer
Location: Irving, TX / Charlotte
Location: Irving, TX / Charlotte
Required Skills: Google Cloud Platform, 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).