Gen AI Developer with Java

Job Role – Gen AI Developer with JavaPlano, TX – 5 Days OnsiteRequired Skills & ExperienceTechnical SkillsLanguages: 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.Experience8 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.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.Key ResponsibilitiesAI 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.