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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # FullStack Developer with Gen AI - **Company:** Voto Consulting LLC - **Location:** Plano, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Microsoft Windows, Apache ActiveMQ, Application Programming Interfaces (APIs), Artificial Intelligence, IBM AIX, Ajax (Programming Language), Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, JIRA, HTML5, Big Data, Unix, Cascading Style Sheets (CSS), Cloud Computing, Code Review, DevOps, Java Platform Enterprise Edition (J2EE), Gradle, JQuery, Python (Programming Language), Machine Learning, Apache Maven, Oracle Databases, Ansible, Software Engineering, Software Systems, SonarQube, Systems Integration, Scripting, Java Application Server, Enterprise Software Applications, Postman, Apache Camel, GitHub Copilot, ReactJS, Large Language Models, Prompt Engineering, Apache Spark, Spring-boot, Generative AI, Git, Event Driven Architecture, Containerization, Kubernetes, HuggingFace, Apache Kafka, Machine Learning Operations, Front End Software Development, Functional Programming, Restful APIs, Cucumber (Software), GPT, Enterprise Service Bus, Docker, Jenkins, Microservices - **Published:** August 12, 2026 - **Apply:** https://www.dice.com/job-detail/8dd39aa9-50c5-413b-8441-155f380805df ## About the Role Technical Skills Languages: Java (8/11/17), Python (scripting & AI automation). AI/ML & LLM: AWS Bedrock, OpenAI GPT-4 API, LangChain, Hugging Face Transformers, RAG pipelines, LLM orchestration, prompt engineering, AI-assisted code review (GitHub Copilot). Frameworks: Spring Boot, Spring AI, Microservices, Apache Camel, Fuse ESB. Messaging: Apache Kafka, ActiveMQ Artemis, event-driven architecture. Frontend: React JS, HTML5, CSS3, AJAX, jQuery. Cloud & DevOps: AWS (EC2, S3, Lambda, SageMaker), Docker, Kubernetes, Jenkins, Ansible, Git. Databases: Oracle (9i/10g/12c), vector databases (Pinecone, others). Testing & Tools: Cucumber (BDD), SonarQube, Postman, JIRA, Maven, Gradle, Apache Spark. Operating Systems: Windows, UNIX, IBM AIX. Experience 8 years in AI/ML engineering and enterprise Java development. Proven track record in deploying ML models and generative AI systems into production. Strong expertise in microservices architecture, Kafka-based integrations, and cloud-native solutions. Demonstrated leadership in mentoring teams and driving engineering best practices ## Description We are seeking a seasoned Generative AI Java Developer with expertise in AWS Bedrock and enterprise-grade Java application development. This role combines advanced AI/ML engineering with robust software development skills to design, build, and deliver scalable, intelligent systems. The ideal candidate will have a proven track record in deploying generative AI solutions, architecting microservices, and integrating AI/LLM capabilities into modern enterprise applications-particularly within banking and financial domains., AI System Design & Implementation: Architect and develop scalable AI agent frameworks tailored to business needs, leveraging foundational models and AWS Bedrock. Bedrock Model Expertise: Fine-tune and deploy Bedrock models (LLMs, multimodal models) for use cases such as vector-based retrieval and Retrieval-Augmented Generation (RAG). Enterprise Java Development: Design, develop, and deliver multi-tier, enterprise-grade Java applications with strong focus on microservices, REST APIs, and Kafka-based integrations. Optimization & Reliability: Enhance model performance while balancing computational cost, ensuring deployed AI systems are robust, accurate, and maintainable. Innovation & Integration: Actively integrate AI/ML capabilities and LLM-based tooling into modern software systems, staying updated on emerging technologies. Collaboration: Work closely with data scientists, engineers, product managers, and stakeholders to translate AI research into production-ready systems. Leadership & Best Practices: Mentor teams, drive engineering excellence through TDD, BDD, and DDD, and ensure delivery of high-quality software solutions. Data & Infrastructure: Manage large datasets, model lifecycle tools (MLflow, Kubeflow), and containerization (Docker, Kubernetes) for reproducible, scalable environments. ## Related Videos - [The Data Phoenix: The future of the Internet and the Open Web](https://www.wearedevelopers.com/videos/1116-the-data-phoenix-the-future-of-the-internet-and-the-open-web) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [Improving quality with Agentic AI with Rovo Dev and Xray](https://www.wearedevelopers.com/videos/2005-improving-quality-with-agentic-ai-with-rovo-dev-and-xray) - [One AI API to Power Them All](https://www.wearedevelopers.com/videos/1601-one-ai-api-to-power-them-all) - [Git for Code Reviews](https://www.wearedevelopers.com/videos/429-git-for-code-reviews) ## Related Articles - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [How We Built a Worry-Free System That Runs for 10+ Years – And What We’d Do Again](https://www.wearedevelopers.com/magazine/751-how-we-built-a-worry-free-system-that-runs-for-10-years-and-what-we-d-do-again) - [Dev Digest 137 - AI'm not sure about this](https://www.wearedevelopers.com/magazine/485-dev-digest-137-ai-m-not-sure-about-this) - [Dev Digest 132 - Binging WADFlix?](https://www.wearedevelopers.com/magazine/473-dev-digest-132-binging-wadflix)