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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # GenAI Developer - **Company:** Yantran 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, Automation of Tests, Big Data, Unix, Cascading Style Sheets (CSS), Cloud Computing, Code Review, Data Infrastructure, DevOps, Java Platform Enterprise Edition (J2EE), Gradle, JQuery, Python (Programming Language), Machine Learning, Apache Maven, Oracle Databases, OpenAI, Ansible, Software Engineering, Software Systems, SonarQube, Systems Integration, Pinecone, Java Application Server, Enterprise Software Applications, Postman, Apache Camel, GitHub Copilot, LangChain, ReactJS, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Spring-boot, Generative AI, Agentic-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:** November 3, 2026 - **Apply:** https://www.thejobnetwork.com/job/d7f21df9-3b09-4069-847e-49febb56427c/genai-developer ## About the Role 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., 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 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., AI tools may assist in the recruitment process; however, all hiring decisions are made by the recruitment team based on a comprehensive evaluation of candidates." *** is an Equal Employment Opportunity employer. We promote and support a diverse workforce at all levels of the company. All qualified applicants will receive consideration for employment without regard to race, religion, color, sex, age, national origin, or disability. All applicants will be evaluated solely on the basis of their ability, competence, and performance of the essential functions of their positions with or without reasonable accommodations. Reasonable accommodations also are available in the hiring process for applicants with disabilities. Candidates can request a reasonable accommodation by contacting the company ADA Coordinator at *** .