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# Agentic AI developers

- **Company:** Peoplentech Llc
- **Location:** Irving, TX, United States
- **Experience:** Experienced
- **Salary:** $149,760.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, BigQuery, Databases, Distributed Systems, Python (Programming Language), Machine Learning, Software Engineering, Data Logging, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Prompt Engineering, Spring-boot, Generative AI, Backend, Agentic-AI, Google Cloud Functions, Google Gemini, Restful APIs, Microservices
- **Published:** October 29, 2026
- **Apply:** https://www.thejobnetwork.com/job/e5de3ceb-523d-415a-b72b-217672fd844c/agentic-ai-developers

## About the Role

· 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 of experience in developing AI/ML or Generative AI projects
Core Technical Skills
· Hands on experience on Google ADK, MCP servers, agent to agent communication, agent orchestration.
· Java (4 ), 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)

## Description

· Enable agents to interact with enterprise systems (APIs, databases, tools)
Java & Backend Engineering
· 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
GCP & GenAI Implementation
· 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
LLM, RAG & Context Engineering
· Implement Retrieval-Augmented Generation (RAG) pipelines
· Build context-aware systems using vector search and embeddings
· Optimize prompts and workflows for performance and cost
MLOps & Deployment
· Deploy and monitor AI models and agents in production
· Implement CI/CD pipelines, logging, and observability
· Ensure reliability, safety, and guardrails for AI systems