Job Title: GCP AI Engineer (LLMs, LangChain, LangGraph, Java/Python)

Location: Irving, TX, or Charlotte, NC (Hybrid)

Type: Contract

Duration: Long term

Job Description:

• We are seeking an innovative and highly skilled AI Engineer to join our dynamic team.
• The ideal candidate will bridge the gap between traditional software engineering and cutting-edge artificial intelligence.
• You will be instrumental in designing, building, and deploying advanced AI agents, working closely with Large Language Models (LLMs), and driving automated code generation initiatives.
• If you have a strong foundation in Java and Python, coupled with hands-on experience using Google's AI tools, we want you to help us build the next generation of intelligent applications

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, Fast API for Python).
• Familiarity with containerization and orchestration (Docker, Kubernetes).
• Experience with vector databases (e.g., Pinecone, Weaviate, Milvus).