GCP AI Engineer

Role : GCP AI Engineer

Location : Irving, TX, or Charlotte (100% Onsite)

Contract

Note: Programming Languages: Strong proficiency in both Java and Python, with a proven track record of building production-grade software.

Key Responsibilities:

• 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.

Must-Have Qualifications:

• 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.

Good-to-Have (Optional but highly valued):

• 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).