FullStack Developer with Gen AI

Voto Consulting LLC

Plano, TX, United States

25 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

8 years minimum

Working hours

Regular working hours

Job source

www.dice.com

Tech stack

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

+46 more

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

Job 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.

Requirements

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