JOBSEARCHER

Java + AI Engineer

ARCHIVED
ArtechCharlotte, NCMay 28th, 2026

We can't find an active application page for this role right now. It may reopen or be listed elsewhere. Use Next Steps to search for an active apply link and similar live jobs.

Java + AI EngineerWe are seeking a Java + AI Engineer to design, develop, and deploy AI/ML and Generative AI solutions, including LLM-based applications, RAG pipelines, predictive models, and AI agents. The ideal candidate will translate business requirements into production-ready AI solutions while ensuring scalability, security, and compliance.Key Responsibilities:Design, develop, and deploy Generative AI/ML solutions, including LLM-based applications, retrieval-augmented generation (RAG) pipelines, embeddings, and AI agents.Translate business use cases into production-ready AI solutions with measurable outcomes.Implement LLM orchestration, prompt engineering, vector search, and model fine-tuning.Develop scalable APIs and microservices to integrate AI capabilities into enterprise applications.Collaborate with Data Engineers, Data Scientists, Product Owners, and Cloud teams across onshore/offshore models.Implement MLOps / LLMOps practices, including CI/CD, monitoring, versioning, model governance, and observability.Ensure Responsible AI, security, compliance, and data privacy by design.Support production deployments, performance tuning, and continuous improvement of AI systems.Required Skills:Experience in software engineering, ML engineering, or AI solution development.Strong proficiency in Java, including Java Streams.Hands-on experience with Generative AI / LLMs, including RAG, embeddings, prompt engineering, and agents.Solid understanding of data engineering concepts, SQL/NoSQL, and feature pipelines.Experience deploying AI solutions on cloud platforms (GCP preferred; AWS/Azure acceptable).Familiarity with Docker, Kubernetes, and CI/CD pipelines.Strong problem-solving, communication, and stakeholder collaboration skills.Preferred Skills:Experience with MLOps/LLMOps frameworks.Knowledge of enterprise AI governance and compliance standards.Exposure to cross-functional agile teams and production AI deployment environments.