GCP AI Engineer
- E-Solutions
2 Hours Ago
NA
C2C
Irving-TX
5-10 Years
Required Skills: Docker, Kubernetes, Spring Boot ,Java, FastAPI, Python, Google Cloud AI/Vertex AI
Job Description
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).
