Google Cloud Platform AI Engineer ---- Only W2

Job Summary:
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, FastAPI for Python).
Familiarity with containerization and orchestration (Docker, Kubernetes).
Experience with vector databases (e.g., Pinecone, Weaviate, Milvus).