Full Stack AI Developers

Ampcus Inc.Irving, TX
Onsite

About The Position

Ampcus Inc. is seeking a motivated Full Stack AI Developer to join their team. This role involves designing, developing, and deploying AI agents and backend systems, with a focus on LLM-based agents, Java backend development, and Google Cloud Platform (GCP) services. The developer will be responsible for building scalable applications, integrating AI agents with enterprise systems, and ensuring the reliability and performance of AI solutions.

Requirements

  • 3–7 years of software development experience
  • Strong hands-on experience in Python
  • 2 years experience building and deploying real-world agents in Production environment
  • Strong hands-on experience in Java backend development (4 years exp)
  • Hands-on experience in developing AI/ML or Generative AI projects
  • Hands-on experience on Google ADK, MCP servers, agent to agent communication, agent orchestration
  • Java (4 years experience)
  • Spring Boot
  • Microservices
  • REST APIs and distributed systems design
  • Hands-on experience on Python, LLMs, GenAI, and agent-based systems
  • Hands-on experience with GCP services (Vertex AI preferred)

Responsibilities

  • Design and develop LLM-based AI agents capable of reasoning, planning, and task execution.
  • Build and manage multi-agent orchestration workflows.
  • Enable agents to interact with enterprise systems (APIs, databases, tools).
  • Develop scalable backend applications using Java, Spring Boot, and Microservices.
  • Build REST APIs to support AI-driven workflows and integrations.
  • Ensure performance, scalability, and maintainability of services.
  • Develop AI solutions using Vertex AI, Gemini, BigQuery, Cloud Run, and Pub/Sub.
  • Work with Vertex AI Agent Builder and Google ADK for agent development.
  • Implement Retrieval-Augmented Generation (RAG) pipelines.
  • Build context-aware systems using vector search and embeddings.
  • Optimize prompts and workflows for performance and cost.
  • Deploy and monitor AI models and agents in production.
  • Implement CI/CD pipelines, logging, and observability.
  • Ensure reliability, safety, and guardrails for AI systems.
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