Overview
Contract - W2
Contract - 5 day((s))
Skills
langGraph
Experience with Google ADK
or MCP. Prior experience building context-aware agentic systems or autonomous AI agents.
Job Details
Job Summary (Full Stack Developer Agentic AI Stack Experience):
- Design, develop, and maintain scalable backend services and APIs using object-oriented programming languages (e.g., Python, Ruby, Go, Swift, Java).
- Develop and implement full-stack applications, including front-end components with Angular or React.
- Utilize Google Cloud Platform (Google Cloud Platform) native tools (BigQuery, CloudStorage, Dataflow, GKE, Cloud Run, Pub/Sub, Vertex AI) for solution development.
- Design and implement machine learning (ML) pipelines and services, following MLOps best practices.
- Work with large language models (LLMs) and Generative AI models on Google Cloud Platform.
- Apply Agentic AI frameworks such as Google ADK, LangGraph, or Model Context Protocol (MCP).
- Use prompt engineering, context management, and embedding techniques for advanced application behavior.
- Ensure strong proficiency in Python and experience developing RESTful or GraphQL APIs and microservices.
- Demonstrate hands-on experience with Google Cloud Platform services, particularly for AI and ML workloads.
- Apply knowledge of the AI/ML lifecycle using tools like TensorFlow, PyTorch, and BigQuery ML.
- Implement MLOps tools and practices for robust and scalable AI/ML solutions.
- Contribute front-end development expertise using frameworks like Angular or React.
- Leverage experience with Agentic AI libraries and protocols for intelligent, autonomous systems.
- (Nice to have) Prior experience with Google ADK, LangGraph, MCP, or building context-aware agentic or autonomous AI systems.
- Design, develop, and maintain scalable backend services and APIs using object-oriented programming languages (e.g., Python, Ruby, Go, Swift, Java).
- Develop and implement full-stack applications, including front-end components with Angular or React.
- Utilize Google Cloud Platform (Google Cloud Platform) native tools (BigQuery, CloudStorage, Dataflow, GKE, Cloud Run, Pub/Sub, Vertex AI) for solution development.
- Design and implement machine learning (ML) pipelines and services, following MLOps best practices.
- Work with large language models (LLMs) and Generative AI models on Google Cloud Platform.
- Apply Agentic AI frameworks such as Google ADK, LangGraph, or Model Context Protocol (MCP).
- Use prompt engineering, context management, and embedding techniques for advanced application behavior.
- Ensure strong proficiency in Python and experience developing RESTful or GraphQL APIs and microservices.
- Demonstrate hands-on experience with Google Cloud Platform services, particularly for AI and ML workloads.
- Apply knowledge of the AI/ML lifecycle using tools like TensorFlow, PyTorch, and BigQuery ML.
- Implement MLOps tools and practices for robust and scalable AI/ML solutions.
- Contribute front-end development expertise using frameworks like Angular or React.
- Leverage experience with Agentic AI libraries and protocols for intelligent, autonomous systems.
- (Nice to have) Prior experience with Google ADK, LangGraph, MCP, or building context-aware agentic or autonomous AI systems.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.