> Markdown version of [/jobs/ext/2988015-java-developer-with-agentic-ai](https://www.wearedevelopers.com/jobs/ext/2988015-java-developer-with-agentic-ai). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Java Developer with Agentic AI - **Company:** CA-One Tech Cloud Inc. - **Location:** Dallas, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Architectural Patterns, Automation of Tests, Microsoft Azure, Code Generation, Code Review, Continuous Integration, Software Design Patterns, Fault Tolerance, Graph Database, Python (Programming Language), Neo4j, Software Tools, Software Engineering, SPARQL, Systems Integration, Google Cloud, Spring Cloud, Large Language Models, Multi-Agent Systems, Prompt Engineering, Spring-boot, State Machines, Backend, Event Driven Architecture, Kubernetes, Enterprise Integration, Apache Kafka, Virtual Agents, Functional Programming, Restful APIs, Terraform, Stream Processing, Microservices - **Published:** September 18, 2026 - **Apply:** https://www.dice.com/job-detail/35fdbe38-4fd8-40f9-8789-e294e07aeeba ## About the Role * Java & Spring Boot: Deep expertise in Java (12+ preferred), Spring Boot, Spring Cloud, RESTful APIs, and microservices design patterns. * Event-Driven Architecture: Strong hands-on experience with Apache Kafka (producers, consumers, stream processing, topic architecture, and fault tolerance). * Enterprise Delivery: Solid track record of deploying resilient, high-scale applications into production environments. AI & Agentic Capabilities * Python Proficiency: Strong functional programming capability in Python for AI framework integration. * Agentic Frameworks: Hands-on experience building cognitive state machines or multi-agent systems using LangGraph (or similar orchestration frameworks like LangChain/AutoGPT). * Knowledge Graphs: Experience with graph databases (e.g., Neo4j, Amazon Neptune, NetworkX) or semantic tech (RDF, SPARQL, Cypher) to represent contextual enterprise knowledge. * Modern AI Engineering Tools: Deep hands-on experience using Claude (Claude 3.5 Sonnet / API workflows), Devin, or AntiGravity CLI for code generation, architectural refactoring, and automated development tasks. Process & Frameworks * ADLC Exposure: Comprehensive understanding of AI-Driven Development Life Cycles moving beyond basic prompt engineering into automated task breakdown, AI-assisted code reviews, automated refactoring, and continuous integration. Nice to Have * Experience with Vector Databases (Pinecone, Qdrant, Milvus) and hybrid search implementations (Graph + Vector / GraphRAG). * Exposure to cloud-native deployments (AWS, Azure, or Google Cloud Platform) using Kubernetes and Terraform. * Familiarity with evaluation frameworks for LLMs and agentic systems (e.g., Ragas, TruLens). ## Description * Enterprise Integration & Architecture: Design, build, and maintain production-ready microservices using Java and Spring Boot, with high-performance event-driven pipelines powered by Apache Kafka. * Agentic AI & Knowledge Architecture: Build multi-agent workflows and complex orchestration pipelines using Lang Graph and Python. Model domain expertise using Knowledge Graphs to ground AI outputs in structured enterprise data. * ADLC Transformation: Champion and operationalize AI-Driven Software Development Life Cycle (ADLC) frameworks to accelerate engineering throughput, maintain high code quality, and automate automated testing/deployment loops. * Hands-on AI Tooling: Daily hands-on development leveraging AI coding agents and including in any one of the LLMs - Claude, Devin, and Antigravity CLI to automate complex code bases and workflow automation. * CoE Enablement & Standards: Establish best practices, architecture patterns, and governance frameworks for integrating GenAI and Agentic systems with existing core backend services across the organization. ## Related Videos - [Java Meets AI: Empowering Spring Developers to Build Intelligent Apps](https://www.wearedevelopers.com/videos/1554-java-meets-ai-empowering-spring-developers-to-build-intelligent-apps) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [Supercharge Agentic AI Apps: A DevEx-Driven Approach to Cloud-Native Scaffolding](https://www.wearedevelopers.com/videos/1604-supercharge-agentic-ai-apps-a-devex-driven-approach-to-cloud-native-scaffolding) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) - [Cyber Sleuth: Finding Hidden Connections in Cyber Data](https://www.wearedevelopers.com/videos/893-cyber-sleuth-finding-hidden-connections-in-cyber-data) ## Related Articles - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path)