Gen AI Developer

Benefits:

• Dental insurance
• Health insurance
• Vision insurance

Gen AI Developer

Plano, TX - 5 Days Onsite

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.
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 Responsibilities
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.